diff --git a/perception/autoware_detection_by_tracker/package.xml b/perception/autoware_detection_by_tracker/package.xml index 6f23baeefbd3e..651dfadb05a0a 100644 --- a/perception/autoware_detection_by_tracker/package.xml +++ b/perception/autoware_detection_by_tracker/package.xml @@ -24,6 +24,9 @@ tf2_geometry_msgs tf2_ros tier4_perception_msgs +<<<<<<< HEAD +======= +>>>>>>> original/main ament_lint_auto autoware_lint_common diff --git a/perception/autoware_detection_by_tracker/src/tracker/tracker_handler.cpp b/perception/autoware_detection_by_tracker/src/tracker/tracker_handler.cpp index 9c7d801332bb8..4d635b9e836e9 100644 --- a/perception/autoware_detection_by_tracker/src/tracker/tracker_handler.cpp +++ b/perception/autoware_detection_by_tracker/src/tracker/tracker_handler.cpp @@ -23,12 +23,20 @@ namespace autoware::detection_by_tracker { void TrackerHandler::onTrackedObjects( +<<<<<<< HEAD + const autoware_perception_msgs::msg::TrackedObjects::ConstSharedPtr msg) +======= const autoware_perception_msgs::msg::TrackedObjects::ConstSharedPtr input_objects_msg) +>>>>>>> original/main { constexpr size_t max_buffer_size = 10; // Add tracked objects to buffer +<<<<<<< HEAD + objects_buffer_.push_front(*msg); +======= objects_buffer_.push_front(*input_objects_msg); +>>>>>>> original/main // Remove old data while (max_buffer_size < objects_buffer_.size()) { diff --git a/perception/autoware_ground_segmentation/CMakeLists.txt b/perception/autoware_ground_segmentation/CMakeLists.txt index 9cf256b9492bb..298e108b408e3 100644 --- a/perception/autoware_ground_segmentation/CMakeLists.txt +++ b/perception/autoware_ground_segmentation/CMakeLists.txt @@ -104,7 +104,11 @@ if(BUILD_TESTING) test/test_ransac_ground_filter.cpp ) target_link_libraries(test_ransac_ground_filter +<<<<<<< HEAD:perception/ground_segmentation/CMakeLists.txt + ground_segmentation +======= autoware_ground_segmentation +>>>>>>> original/main:perception/autoware_ground_segmentation/CMakeLists.txt ${YAML_CPP_LIBRARIES}) endif() diff --git a/perception/autoware_ground_segmentation/launch/scan_ground_filter.launch.py b/perception/autoware_ground_segmentation/launch/scan_ground_filter.launch.py index 6c85fb9302fdc..b1aa16747a719 100644 --- a/perception/autoware_ground_segmentation/launch/scan_ground_filter.launch.py +++ b/perception/autoware_ground_segmentation/launch/scan_ground_filter.launch.py @@ -35,7 +35,11 @@ def launch_setup(context, *args, **kwargs): nodes = [ ComposableNode( +<<<<<<< HEAD:perception/ground_segmentation/launch/scan_ground_filter.launch.py + package="ground_segmentation", +======= package="autoware_ground_segmentation", +>>>>>>> original/main:perception/autoware_ground_segmentation/launch/scan_ground_filter.launch.py plugin="autoware::ground_segmentation::ScanGroundFilterComponent", name="scan_ground_filter", remappings=[ diff --git a/perception/autoware_ground_segmentation/src/ransac_ground_filter/node.cpp b/perception/autoware_ground_segmentation/src/ransac_ground_filter/node.cpp index dd5ce96a858cd..774c19e866357 100644 --- a/perception/autoware_ground_segmentation/src/ransac_ground_filter/node.cpp +++ b/perception/autoware_ground_segmentation/src/ransac_ground_filter/node.cpp @@ -80,9 +80,16 @@ PlaneBasis getPlaneBasis(const Eigen::Vector3d & plane_normal) return basis; } -using autoware::pointcloud_preprocessor::get_param; - -RANSACGroundFilterComponent::RANSACGroundFilterComponent(const rclcpp::NodeOptions & options) +< < < < < < < < HEAD : perception / ground_segmentation / src / ransac_ground_filter / + node.cpp using pointcloud_preprocessor::get_param; +== == == == using autoware::pointcloud_preprocessor::get_param; +>>>>>>>> original / + main +: perception / + autoware_ground_segmentation / src / ransac_ground_filter / + node.cpp + + RANSACGroundFilterComponent::RANSACGroundFilterComponent(const rclcpp::NodeOptions & options) : Filter("RANSACGroundFilter", options) { base_frame_ = declare_parameter("base_frame", "base_link"); diff --git a/perception/autoware_ground_segmentation/src/ray_ground_filter/node.hpp b/perception/autoware_ground_segmentation/src/ray_ground_filter/node.hpp index 54de09b37ad96..9a2a2d5081a93 100644 --- a/perception/autoware_ground_segmentation/src/ray_ground_filter/node.hpp +++ b/perception/autoware_ground_segmentation/src/ray_ground_filter/node.hpp @@ -58,8 +58,16 @@ #include #endif +<<<<<<<< + HEAD : perception / ground_segmentation / src / ray_ground_filter / node.hpp +#include "gencolors.hpp" +#include "pointcloud_preprocessor/filter.hpp" + == == == == #include "autoware/pointcloud_preprocessor/filter.hpp" #include "gencolors.hpp" + >>>>>>>> original / + main : perception / autoware_ground_segmentation / src / ray_ground_filter / + node.hpp #include #include @@ -75,7 +83,7 @@ #include #include -namespace bg = boost::geometry; + namespace bg = boost::geometry; using Point = bg::model::d2::point_xy; using Polygon = bg::model::polygon; @@ -184,26 +192,33 @@ class RayGroundFilterComponent : public autoware::pointcloud_preprocessor::Filte * @param out_only_indices_cloud_ptr Resulting PointCloud with the indices kept * @param out_removed_indices_cloud_ptr Resulting PointCloud with the indices removed */ - void ExtractPointsIndices( - const PointCloud2::ConstSharedPtr in_cloud_ptr, const pcl::PointIndices & in_indices, + void ExtractPointsIndices(const PointCloud2::ConstSharedPtr in_cloud_ptr, + const pcl::PointIndices & in_indices, < < < < < < < < HEAD + : perception / ground_segmentation / src / ray_ground_filter / + node.hpp PointCloud2::SharedPtr out_only_indices_cloud_ptr, + PointCloud2::SharedPtr out_removed_indices_cloud_ptr); +======== PointCloud2::SharedPtr ground_cloud_msg_ptr, PointCloud2::SharedPtr no_ground_cloud_msg_ptr); +>>>>>>>> original / main : perception / autoware_ground_segmentation / src / ray_ground_filter / + node.hpp - boost::optional calcPointVehicleIntersection(const Point & point); + boost::optional + calcPointVehicleIntersection(const Point & point); - void setVehicleFootprint( - const double min_x, const double max_x, const double min_y, const double max_y); - void initializePointCloud2( - const PointCloud2::ConstSharedPtr & in_cloud_ptr, - const PointCloud2::SharedPtr & out_cloud_msg_ptr); - /** \brief Parameter service callback result : needed to be hold */ - OnSetParametersCallbackHandle::SharedPtr set_param_res_; +void setVehicleFootprint( + const double min_x, const double max_x, const double min_y, const double max_y); +void initializePointCloud2( + const PointCloud2::ConstSharedPtr & in_cloud_ptr, + const PointCloud2::SharedPtr & out_cloud_msg_ptr); +/** \brief Parameter service callback result : needed to be hold */ +OnSetParametersCallbackHandle::SharedPtr set_param_res_; - /** \brief Parameter service callback */ - rcl_interfaces::msg::SetParametersResult paramCallback(const std::vector & p); +/** \brief Parameter service callback */ +rcl_interfaces::msg::SetParametersResult paramCallback(const std::vector & p); public: - EIGEN_MAKE_ALIGNED_OPERATOR_NEW - explicit RayGroundFilterComponent(const rclcpp::NodeOptions & options); +EIGEN_MAKE_ALIGNED_OPERATOR_NEW +explicit RayGroundFilterComponent(const rclcpp::NodeOptions & options); }; } // namespace autoware::ground_segmentation diff --git a/perception/autoware_ground_segmentation/src/scan_ground_filter/node.cpp b/perception/autoware_ground_segmentation/src/scan_ground_filter/node.cpp index 2745606697a20..52fd0e71ca11d 100644 --- a/perception/autoware_ground_segmentation/src/scan_ground_filter/node.cpp +++ b/perception/autoware_ground_segmentation/src/scan_ground_filter/node.cpp @@ -32,8 +32,11 @@ using autoware::universe_utils::normalizeDegree; using autoware::universe_utils::normalizeRadian; using autoware::vehicle_info_utils::VehicleInfoUtils; -ScanGroundFilterComponent::ScanGroundFilterComponent(const rclcpp::NodeOptions & options) -: autoware::pointcloud_preprocessor::Filter("ScanGroundFilter", options) +ScanGroundFilterComponent::ScanGroundFilterComponent(const rclcpp::NodeOptions & options)<<<<<<<< + HEAD : perception / ground_segmentation / src / scan_ground_filter / + node.cpp : pointcloud_preprocessor::Filter("ScanGroundFilter", options) == + == == == : autoware::pointcloud_preprocessor::Filter("ScanGroundFilter", options)>>>>>>>> original + / main : perception / autoware_ground_segmentation / src / scan_ground_filter / node.cpp { // set initial parameters { diff --git a/perception/autoware_ground_segmentation/src/scan_ground_filter/node.hpp b/perception/autoware_ground_segmentation/src/scan_ground_filter/node.hpp index ad5d0ea4b6bc6..3f6ad785fa519 100644 --- a/perception/autoware_ground_segmentation/src/scan_ground_filter/node.hpp +++ b/perception/autoware_ground_segmentation/src/scan_ground_filter/node.hpp @@ -15,9 +15,18 @@ #ifndef SCAN_GROUND_FILTER__NODE_HPP_ #define SCAN_GROUND_FILTER__NODE_HPP_ +<<<<<<<< + HEAD : perception / ground_segmentation / src / scan_ground_filter / node.hpp +#include "autoware_vehicle_info_utils/vehicle_info.hpp" +#include "pointcloud_preprocessor/filter.hpp" +#include "pointcloud_preprocessor/transform_info.hpp" + == == == == #include "autoware/pointcloud_preprocessor/filter.hpp" #include "autoware/pointcloud_preprocessor/transform_info.hpp" #include "autoware_vehicle_info_utils/vehicle_info.hpp" + >>>>>>>> original / + main : perception / autoware_ground_segmentation / src / scan_ground_filter / + node.hpp #include @@ -38,7 +47,7 @@ #include #include -class ScanGroundFilterTest; + class ScanGroundFilterTest; namespace autoware::ground_segmentation { diff --git a/perception/autoware_ground_segmentation/test/test_ransac_ground_filter.cpp b/perception/autoware_ground_segmentation/test/test_ransac_ground_filter.cpp index e18c166adb8b3..388812b3d49cd 100644 --- a/perception/autoware_ground_segmentation/test/test_ransac_ground_filter.cpp +++ b/perception/autoware_ground_segmentation/test/test_ransac_ground_filter.cpp @@ -134,8 +134,12 @@ void convertPCL2PointCloud2( TEST_F(RansacGroundFilterTestSuite, TestCase1) { +<<<<<<< HEAD:perception/ground_segmentation/test/test_ransac_ground_filter.cpp + const auto share_dir = ament_index_cpp::get_package_share_directory("ground_segmentation"); +======= const auto share_dir = ament_index_cpp::get_package_share_directory("autoware_ground_segmentation"); +>>>>>>> original/main:perception/autoware_ground_segmentation/test/test_ransac_ground_filter.cpp const auto config_path = share_dir + "/config/ransac_ground_filter.param.yaml"; YAML::Node config = YAML::LoadFile(config_path); auto params = config["/**"]["ros__parameters"]; diff --git a/perception/autoware_image_projection_based_fusion/docs/segmentation-pointcloud-fusion.md b/perception/autoware_image_projection_based_fusion/docs/segmentation-pointcloud-fusion.md index ec2bffca58462..a67b13df99818 100644 --- a/perception/autoware_image_projection_based_fusion/docs/segmentation-pointcloud-fusion.md +++ b/perception/autoware_image_projection_based_fusion/docs/segmentation-pointcloud-fusion.md @@ -32,8 +32,14 @@ The node `segmentation_pointcloud_fusion` is a package for filtering pointcloud ### Core Parameters +<<<<<<< HEAD:perception/image_projection_based_fusion/docs/segmentation-pointcloud-fusion.md +{{ json_to_markdown("perception/image_projection_based_fusion/schema/segmentation_pointcloud_fusion.schema.json") }} +======= + {{ json_to_markdown("perception/autoware_image_projection_based_fusion/schema/segmentation_pointcloud_fusion.schema.json") }} +> > > > > > > original/main:perception/autoware_image_projection_based_fusion/docs/segmentation-pointcloud-fusion.md + ## Assumptions / Known limits +<<<<<<< HEAD:perception/shape_estimation/launch/shape_estimation.launch.xml + +======= +>>>>>>> original/main:perception/autoware_shape_estimation/launch/shape_estimation.launch.xml diff --git a/perception/compare_map_segmentation/src/compare_elevation_map_filter/node.cpp b/perception/compare_map_segmentation/src/compare_elevation_map_filter/node.cpp new file mode 100644 index 0000000000000..e5a19245bc5b1 --- /dev/null +++ b/perception/compare_map_segmentation/src/compare_elevation_map_filter/node.cpp @@ -0,0 +1,101 @@ +// Copyright 2021 Tier IV, Inc. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "node.hpp" + +#include +#include +#include +#include +#include +#include + +#include + +#include +#include +#include +#include +#include // To be replaced by std::filesystem in C++17 + +#include +#include +#include + +namespace autoware::compare_map_segmentation +{ +CompareElevationMapFilterComponent::CompareElevationMapFilterComponent( + const rclcpp::NodeOptions & options) +: Filter("CompareElevationMapFilter", options) +{ + unsubscribe(); + layer_name_ = declare_parameter("map_layer_name"); + height_diff_thresh_ = declare_parameter("height_diff_thresh"); + map_frame_ = declare_parameter("map_frame"); + + rclcpp::QoS durable_qos{1}; + durable_qos.transient_local(); + + sub_map_ = this->create_subscription( + "input/elevation_map", durable_qos, + std::bind( + &CompareElevationMapFilterComponent::elevationMapCallback, this, std::placeholders::_1)); +} + +void CompareElevationMapFilterComponent::elevationMapCallback( + const grid_map_msgs::msg::GridMap::ConstSharedPtr elevation_map) +{ + grid_map::GridMapRosConverter::fromMessage(*elevation_map, elevation_map_); + elevation_map_data_ = elevation_map_.get(layer_name_); + + const float min_value = elevation_map_.get(layer_name_).minCoeffOfFinites(); + const float max_value = elevation_map_.get(layer_name_).maxCoeffOfFinites(); + grid_map::GridMapCvConverter::toImage( + elevation_map_, layer_name_, CV_16UC1, min_value, max_value, elevation_image_); + subscribe(); +} + +void CompareElevationMapFilterComponent::filter( + const PointCloud2ConstPtr & input, [[maybe_unused]] const IndicesPtr & indices, + PointCloud2 & output) +{ + pcl::PointCloud::Ptr pcl_input(new pcl::PointCloud); + pcl::PointCloud::Ptr pcl_output(new pcl::PointCloud); + std::string output_frame = map_frame_; + + output_frame = elevation_map_.getFrameId(); + elevation_map_.setTimestamp(input->header.stamp.nanosec); + pcl::fromROSMsg(*input, *pcl_input); + pcl_output->points.reserve(pcl_input->points.size()); + for (const auto & point : pcl_input->points) { + if (elevation_map_.isInside(grid_map::Position(point.x, point.y))) { + float elevation_value = elevation_map_.atPosition( + layer_name_, grid_map::Position(point.x, point.y), + grid_map::InterpolationMethods::INTER_LINEAR); + const float height_diff = point.z - elevation_value; + if (height_diff > height_diff_thresh_) { + pcl_output->points.push_back(point); + } + } + } + + pcl::toROSMsg(*pcl_output, output); + output.header.stamp = input->header.stamp; + output.header.frame_id = output_frame; +} +} // namespace autoware::compare_map_segmentation + +#include +RCLCPP_COMPONENTS_REGISTER_NODE( + autoware::compare_map_segmentation::CompareElevationMapFilterComponent) diff --git a/perception/compare_map_segmentation/src/compare_elevation_map_filter/node.hpp b/perception/compare_map_segmentation/src/compare_elevation_map_filter/node.hpp new file mode 100644 index 0000000000000..ceddaa67ac2cd --- /dev/null +++ b/perception/compare_map_segmentation/src/compare_elevation_map_filter/node.hpp @@ -0,0 +1,61 @@ +// Copyright 2021 Tier IV, Inc. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef COMPARE_ELEVATION_MAP_FILTER__NODE_HPP_ +#define COMPARE_ELEVATION_MAP_FILTER__NODE_HPP_ + +#include "autoware/pointcloud_preprocessor/filter.hpp" + +#include +#include +#include +#include + +#include + +#include +#include +#include +#include +#include +namespace autoware::compare_map_segmentation +{ +class CompareElevationMapFilterComponent : public autoware::pointcloud_preprocessor::Filter +{ +protected: + void filter( + const PointCloud2ConstPtr & input, const IndicesPtr & indices, PointCloud2 & output) override; + +private: + rclcpp::Subscription::SharedPtr sub_map_; + + rclcpp::Publisher::SharedPtr pub_filtered_cloud_; + grid_map::GridMap elevation_map_; + cv::Mat elevation_image_; + grid_map::Matrix elevation_map_data_; + std::string layer_name_; + std::string map_frame_; + double height_diff_thresh_; + + void setVerbosityLevelToDebugIfFlagSet(); + void processPointcloud(grid_map::GridMapPclLoader * gridMapPclLoader); + void elevationMapCallback(const grid_map_msgs::msg::GridMap::ConstSharedPtr elevation_map); + +public: + PCL_MAKE_ALIGNED_OPERATOR_NEW + explicit CompareElevationMapFilterComponent(const rclcpp::NodeOptions & options); +}; +} // namespace autoware::compare_map_segmentation + +#endif // COMPARE_ELEVATION_MAP_FILTER__NODE_HPP_ diff --git a/perception/compare_map_segmentation/src/distance_based_compare_map_filter/node.cpp b/perception/compare_map_segmentation/src/distance_based_compare_map_filter/node.cpp new file mode 100644 index 0000000000000..53183cd7dc3f0 --- /dev/null +++ b/perception/compare_map_segmentation/src/distance_based_compare_map_filter/node.cpp @@ -0,0 +1,186 @@ +// Copyright 2020 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "node.hpp" + +#include "autoware/universe_utils/ros/debug_publisher.hpp" +#include "autoware/universe_utils/system/stop_watch.hpp" + +#include +#include +#include + +#include + +namespace autoware::compare_map_segmentation +{ + +void DistanceBasedStaticMapLoader::onMapCallback( + const sensor_msgs::msg::PointCloud2::ConstSharedPtr map) +{ + pcl::PointCloud map_pcl; + pcl::fromROSMsg(*map, map_pcl); + const auto map_pcl_ptr = pcl::make_shared>(map_pcl); + + (*mutex_ptr_).lock(); + map_ptr_ = map_pcl_ptr; + *tf_map_input_frame_ = map_ptr_->header.frame_id; + if (!tree_) { + if (map_ptr_->isOrganized()) { + tree_.reset(new pcl::search::OrganizedNeighbor()); + } else { + tree_.reset(new pcl::search::KdTree(false)); + } + } + tree_->setInputCloud(map_ptr_); + + (*mutex_ptr_).unlock(); +} + +bool DistanceBasedStaticMapLoader::is_close_to_map( + const pcl::PointXYZ & point, const double distance_threshold) +{ + if (map_ptr_ == NULL) { + return false; + } + if (tree_ == NULL) { + return false; + } + + std::vector nn_indices(1); + std::vector nn_distances(1); + if (!isFinite(point)) { + return false; + } + if (!tree_->nearestKSearch(point, 1, nn_indices, nn_distances)) { + return false; + } + if (nn_distances[0] > distance_threshold) { + return false; + } + return true; +} + +bool DistanceBasedDynamicMapLoader::is_close_to_map( + const pcl::PointXYZ & point, const double distance_threshold) +{ + if (current_voxel_grid_dict_.size() == 0) { + return false; + } + if (!isFinite(point)) { + return false; + } + + const int map_grid_index = static_cast( + std::floor((point.x - origin_x_) / map_grid_size_x_) + + map_grids_x_ * std::floor((point.y - origin_y_) / map_grid_size_y_)); + + if (static_cast(map_grid_index) >= current_voxel_grid_array_.size()) { + return false; + } + if (current_voxel_grid_array_.at(map_grid_index) != NULL) { + if (current_voxel_grid_array_.at(map_grid_index)->map_cell_kdtree == NULL) { + return false; + } + std::vector nn_indices(1); + std::vector nn_distances(1); + if (!current_voxel_grid_array_.at(map_grid_index) + ->map_cell_kdtree->nearestKSearch(point, 1, nn_indices, nn_distances)) { + return false; + } + if (nn_distances[0] <= distance_threshold) { + return true; + } + } + return false; +} + +DistanceBasedCompareMapFilterComponent::DistanceBasedCompareMapFilterComponent( + const rclcpp::NodeOptions & options) +: Filter("DistanceBasedCompareMapFilter", options) +{ + // initialize debug tool + { + using autoware::universe_utils::DebugPublisher; + using autoware::universe_utils::StopWatch; + stop_watch_ptr_ = std::make_unique>(); + debug_publisher_ = std::make_unique(this, "distance_based_compare_map_filter"); + stop_watch_ptr_->tic("cyclic_time"); + stop_watch_ptr_->tic("processing_time"); + } + + distance_threshold_ = declare_parameter("distance_threshold"); + bool use_dynamic_map_loading = declare_parameter("use_dynamic_map_loading"); + if (use_dynamic_map_loading) { + rclcpp::CallbackGroup::SharedPtr main_callback_group; + main_callback_group = this->create_callback_group(rclcpp::CallbackGroupType::MutuallyExclusive); + distance_based_map_loader_ = std::make_unique( + this, distance_threshold_, &tf_input_frame_, &mutex_, main_callback_group); + } else { + distance_based_map_loader_ = std::make_unique( + this, distance_threshold_, &tf_input_frame_, &mutex_); + } +} + +void DistanceBasedCompareMapFilterComponent::filter( + const PointCloud2ConstPtr & input, [[maybe_unused]] const IndicesPtr & indices, + PointCloud2 & output) +{ + std::scoped_lock lock(mutex_); + stop_watch_ptr_->toc("processing_time", true); + + int point_step = input->point_step; + int offset_x = input->fields[pcl::getFieldIndex(*input, "x")].offset; + int offset_y = input->fields[pcl::getFieldIndex(*input, "y")].offset; + int offset_z = input->fields[pcl::getFieldIndex(*input, "z")].offset; + + output.data.resize(input->data.size()); + output.point_step = point_step; + size_t output_size = 0; + for (size_t global_offset = 0; global_offset < input->data.size(); global_offset += point_step) { + pcl::PointXYZ point{}; + std::memcpy(&point.x, &input->data[global_offset + offset_x], sizeof(float)); + std::memcpy(&point.y, &input->data[global_offset + offset_y], sizeof(float)); + std::memcpy(&point.z, &input->data[global_offset + offset_z], sizeof(float)); + if (distance_based_map_loader_->is_close_to_map(point, distance_threshold_)) { + continue; + } + std::memcpy(&output.data[output_size], &input->data[global_offset], point_step); + output_size += point_step; + } + output.header = input->header; + output.fields = input->fields; + output.data.resize(output_size); + output.height = input->height; + output.width = output_size / point_step / output.height; + output.row_step = output_size / output.height; + output.is_bigendian = input->is_bigendian; + output.is_dense = input->is_dense; + + // add processing time for debug + if (debug_publisher_) { + const double cyclic_time_ms = stop_watch_ptr_->toc("cyclic_time", true); + const double processing_time_ms = stop_watch_ptr_->toc("processing_time", true); + debug_publisher_->publish( + "debug/cyclic_time_ms", cyclic_time_ms); + debug_publisher_->publish( + "debug/processing_time_ms", processing_time_ms); + } +} + +} // namespace autoware::compare_map_segmentation + +#include +RCLCPP_COMPONENTS_REGISTER_NODE( + autoware::compare_map_segmentation::DistanceBasedCompareMapFilterComponent) diff --git a/perception/compare_map_segmentation/src/distance_based_compare_map_filter/node.hpp b/perception/compare_map_segmentation/src/distance_based_compare_map_filter/node.hpp new file mode 100644 index 0000000000000..3ff6dc3a32c7b --- /dev/null +++ b/perception/compare_map_segmentation/src/distance_based_compare_map_filter/node.hpp @@ -0,0 +1,128 @@ +// Copyright 2020 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef DISTANCE_BASED_COMPARE_MAP_FILTER__NODE_HPP_ +#define DISTANCE_BASED_COMPARE_MAP_FILTER__NODE_HPP_ + +#include "../voxel_grid_map_loader/voxel_grid_map_loader.hpp" +<<<<<<<< + HEAD : perception / compare_map_segmentation / src / distance_based_compare_map_filter / node.hpp +#include "pointcloud_preprocessor/filter.hpp" + == == == == +#include "autoware/pointcloud_preprocessor/filter.hpp" + >>>>>>>> original / + main : perception / autoware_compare_map_segmentation / src / distance_based_compare_map_filter / + node.hpp + +#include // for pcl::isFinite +#include +#include + +#include +#include +#include + + namespace autoware::compare_map_segmentation +{ + typedef typename pcl::Filter::PointCloud PointCloud; + typedef typename PointCloud::Ptr PointCloudPtr; + typedef typename PointCloud::ConstPtr PointCloudConstPtr; + + class DistanceBasedStaticMapLoader : public VoxelGridStaticMapLoader + { + private: + PointCloudConstPtr map_ptr_; + pcl::search::Search::Ptr tree_; + + public: + DistanceBasedStaticMapLoader( + rclcpp::Node * node, double leaf_size, std::string * tf_map_input_frame, std::mutex * mutex) + : VoxelGridStaticMapLoader(node, leaf_size, 1.0, tf_map_input_frame, mutex) + { + RCLCPP_INFO(logger_, "DistanceBasedStaticMapLoader initialized.\n"); + } + + void onMapCallback(const sensor_msgs::msg::PointCloud2::ConstSharedPtr map) override; + bool is_close_to_map(const pcl::PointXYZ & point, const double distance_threshold) override; + }; + + class DistanceBasedDynamicMapLoader : public VoxelGridDynamicMapLoader + { + public: + DistanceBasedDynamicMapLoader( + rclcpp::Node * node, double leaf_size, std::string * tf_map_input_frame, std::mutex * mutex, + rclcpp::CallbackGroup::SharedPtr main_callback_group) + : VoxelGridDynamicMapLoader( + node, leaf_size, 1.0, tf_map_input_frame, mutex, main_callback_group) + { + RCLCPP_INFO(logger_, "DistanceBasedDynamicMapLoader initialized.\n"); + } + bool is_close_to_map(const pcl::PointXYZ & point, const double distance_threshold) override; + + inline void addMapCellAndFilter( + const autoware_map_msgs::msg::PointCloudMapCellWithID & map_cell_to_add) override + { + map_grid_size_x_ = map_cell_to_add.metadata.max_x - map_cell_to_add.metadata.min_x; + map_grid_size_y_ = map_cell_to_add.metadata.max_y - map_cell_to_add.metadata.min_y; + + pcl::PointCloud map_cell_pc_tmp; + pcl::fromROSMsg(map_cell_to_add.pointcloud, map_cell_pc_tmp); + + auto map_cell_voxel_input_tmp_ptr = + std::make_shared>(map_cell_pc_tmp); + + MapGridVoxelInfo current_voxel_grid_list_item; + current_voxel_grid_list_item.min_b_x = map_cell_to_add.metadata.min_x; + current_voxel_grid_list_item.min_b_y = map_cell_to_add.metadata.min_y; + current_voxel_grid_list_item.max_b_x = map_cell_to_add.metadata.max_x; + current_voxel_grid_list_item.max_b_y = map_cell_to_add.metadata.max_y; + + // add kdtree + pcl::search::Search::Ptr tree_tmp; + if (!tree_tmp) { + if (map_cell_voxel_input_tmp_ptr->isOrganized()) { + tree_tmp.reset(new pcl::search::OrganizedNeighbor()); + } else { + tree_tmp.reset(new pcl::search::KdTree(false)); + } + } + tree_tmp->setInputCloud(map_cell_voxel_input_tmp_ptr); + current_voxel_grid_list_item.map_cell_kdtree = tree_tmp; + + // add + (*mutex_ptr_).lock(); + current_voxel_grid_dict_.insert({map_cell_to_add.cell_id, current_voxel_grid_list_item}); + (*mutex_ptr_).unlock(); + } + }; + + class DistanceBasedCompareMapFilterComponent : public autoware::pointcloud_preprocessor::Filter + { + protected: + virtual void filter( + const PointCloud2ConstPtr & input, const IndicesPtr & indices, PointCloud2 & output); + + private: + double distance_threshold_; + std::unique_ptr distance_based_map_loader_; + + public: + PCL_MAKE_ALIGNED_OPERATOR_NEW + explicit DistanceBasedCompareMapFilterComponent(const rclcpp::NodeOptions & options); + }; +} // namespace autoware::compare_map_segmentation + +// clang-format off +#endif // DISTANCE_BASED_COMPARE_MAP_FILTER__NODE_HPP_ // NOLINT +// clang-format on diff --git a/perception/compare_map_segmentation/src/voxel_based_approximate_compare_map_filter/node.cpp b/perception/compare_map_segmentation/src/voxel_based_approximate_compare_map_filter/node.cpp new file mode 100644 index 0000000000000..8bc22f8c31184 --- /dev/null +++ b/perception/compare_map_segmentation/src/voxel_based_approximate_compare_map_filter/node.cpp @@ -0,0 +1,155 @@ +// Copyright 2020 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "node.hpp" + +#include "autoware/universe_utils/ros/debug_publisher.hpp" +#include "autoware/universe_utils/system/stop_watch.hpp" + +#include +#include +#include + +#include + +namespace autoware::compare_map_segmentation +{ + +bool VoxelBasedApproximateStaticMapLoader::is_close_to_map( + const pcl::PointXYZ & point, [[maybe_unused]] const double distance_threshold) +{ + if (voxel_map_ptr_ == NULL) { + return false; + } + const int index = + voxel_grid_.getCentroidIndexAt(voxel_grid_.getGridCoordinates(point.x, point.y, point.z)); + if (index == -1) { + return false; + } else { + return true; + } +} + +bool VoxelBasedApproximateDynamicMapLoader::is_close_to_map( + const pcl::PointXYZ & point, [[maybe_unused]] const double distance_threshold) +{ + if (current_voxel_grid_dict_.size() == 0) { + return false; + } + + const int map_grid_index = static_cast( + std::floor((point.x - origin_x_) / map_grid_size_x_) + + map_grids_x_ * std::floor((point.y - origin_y_) / map_grid_size_y_)); + + if (static_cast(map_grid_index) >= current_voxel_grid_array_.size()) { + return false; + } + if (current_voxel_grid_array_.at(map_grid_index) != NULL) { + const int index = current_voxel_grid_array_.at(map_grid_index) + ->map_cell_voxel_grid.getCentroidIndexAt( + current_voxel_grid_array_.at(map_grid_index) + ->map_cell_voxel_grid.getGridCoordinates(point.x, point.y, point.z)); + if (index == -1) { + return false; + } else { + return true; + } + } + return false; +} + +VoxelBasedApproximateCompareMapFilterComponent::VoxelBasedApproximateCompareMapFilterComponent( + const rclcpp::NodeOptions & options) +: Filter("VoxelBasedApproximateCompareMapFilter", options) +{ + // initialize debug tool + { + using autoware::universe_utils::DebugPublisher; + using autoware::universe_utils::StopWatch; + stop_watch_ptr_ = std::make_unique>(); + debug_publisher_ = + std::make_unique(this, "voxel_based_approximate_compare_map_filter"); + stop_watch_ptr_->tic("cyclic_time"); + stop_watch_ptr_->tic("processing_time"); + } + + distance_threshold_ = declare_parameter("distance_threshold"); + bool use_dynamic_map_loading = declare_parameter("use_dynamic_map_loading"); + double downsize_ratio_z_axis = declare_parameter("downsize_ratio_z_axis"); + if (downsize_ratio_z_axis <= 0.0) { + RCLCPP_ERROR(this->get_logger(), "downsize_ratio_z_axis should be positive"); + return; + } + if (use_dynamic_map_loading) { + rclcpp::CallbackGroup::SharedPtr main_callback_group; + main_callback_group = this->create_callback_group(rclcpp::CallbackGroupType::MutuallyExclusive); + voxel_based_approximate_map_loader_ = std::make_unique( + this, distance_threshold_, downsize_ratio_z_axis, &tf_input_frame_, &mutex_, + main_callback_group); + } else { + voxel_based_approximate_map_loader_ = std::make_unique( + this, distance_threshold_, downsize_ratio_z_axis, &tf_input_frame_, &mutex_); + } +} + +void VoxelBasedApproximateCompareMapFilterComponent::filter( + const PointCloud2ConstPtr & input, [[maybe_unused]] const IndicesPtr & indices, + PointCloud2 & output) +{ + std::scoped_lock lock(mutex_); + stop_watch_ptr_->toc("processing_time", true); + int point_step = input->point_step; + int offset_x = input->fields[pcl::getFieldIndex(*input, "x")].offset; + int offset_y = input->fields[pcl::getFieldIndex(*input, "y")].offset; + int offset_z = input->fields[pcl::getFieldIndex(*input, "z")].offset; + + output.data.resize(input->data.size()); + output.point_step = point_step; + size_t output_size = 0; + for (size_t global_offset = 0; global_offset < input->data.size(); global_offset += point_step) { + pcl::PointXYZ point{}; + std::memcpy(&point.x, &input->data[global_offset + offset_x], sizeof(float)); + std::memcpy(&point.y, &input->data[global_offset + offset_y], sizeof(float)); + std::memcpy(&point.z, &input->data[global_offset + offset_z], sizeof(float)); + if (voxel_based_approximate_map_loader_->is_close_to_map(point, distance_threshold_)) { + continue; + } + std::memcpy(&output.data[output_size], &input->data[global_offset], point_step); + output_size += point_step; + } + output.header = input->header; + output.fields = input->fields; + output.data.resize(output_size); + output.height = input->height; + output.width = output_size / point_step / output.height; + output.row_step = output_size / output.height; + output.is_bigendian = input->is_bigendian; + output.is_dense = input->is_dense; + + // add processing time for debug + if (debug_publisher_) { + const double cyclic_time_ms = stop_watch_ptr_->toc("cyclic_time", true); + const double processing_time_ms = stop_watch_ptr_->toc("processing_time", true); + debug_publisher_->publish( + "debug/cyclic_time_ms", cyclic_time_ms); + debug_publisher_->publish( + "debug/processing_time_ms", processing_time_ms); + } +} + +} // namespace autoware::compare_map_segmentation + +#include +RCLCPP_COMPONENTS_REGISTER_NODE( + autoware::compare_map_segmentation::VoxelBasedApproximateCompareMapFilterComponent) diff --git a/perception/compare_map_segmentation/src/voxel_based_approximate_compare_map_filter/node.hpp b/perception/compare_map_segmentation/src/voxel_based_approximate_compare_map_filter/node.hpp new file mode 100644 index 0000000000000..653f6bf821f69 --- /dev/null +++ b/perception/compare_map_segmentation/src/voxel_based_approximate_compare_map_filter/node.hpp @@ -0,0 +1,86 @@ +// Copyright 2020 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef VOXEL_BASED_APPROXIMATE_COMPARE_MAP_FILTER__NODE_HPP_ // NOLINT +#define VOXEL_BASED_APPROXIMATE_COMPARE_MAP_FILTER__NODE_HPP_ // NOLINT + +#include "../voxel_grid_map_loader/voxel_grid_map_loader.hpp" +<<<<<<<< + HEAD : perception / compare_map_segmentation / src / voxel_based_approximate_compare_map_filter / + node.hpp +#include "pointcloud_preprocessor/filter.hpp" + == == == == +#include "autoware/pointcloud_preprocessor/filter.hpp" + >>>>>>>> original / + main : perception / autoware_compare_map_segmentation / src / + voxel_based_approximate_compare_map_filter / + node.hpp + +#include +#include + +#include +#include +#include + + namespace autoware::compare_map_segmentation +{ + class VoxelBasedApproximateStaticMapLoader : public VoxelGridStaticMapLoader + { + public: + explicit VoxelBasedApproximateStaticMapLoader( + rclcpp::Node * node, double leaf_size, double downsize_ratio_z_axis, + std::string * tf_map_input_frame, std::mutex * mutex) + : VoxelGridStaticMapLoader(node, leaf_size, downsize_ratio_z_axis, tf_map_input_frame, mutex) + { + RCLCPP_INFO(logger_, "VoxelBasedApproximateStaticMapLoader initialized.\n"); + } + bool is_close_to_map(const pcl::PointXYZ & point, const double distance_threshold) override; + }; + + class VoxelBasedApproximateDynamicMapLoader : public VoxelGridDynamicMapLoader + { + public: + VoxelBasedApproximateDynamicMapLoader( + rclcpp::Node * node, double leaf_size, double downsize_ratio_z_axis, + std::string * tf_map_input_frame, std::mutex * mutex, + rclcpp::CallbackGroup::SharedPtr main_callback_group) + : VoxelGridDynamicMapLoader( + node, leaf_size, downsize_ratio_z_axis, tf_map_input_frame, mutex, main_callback_group) + { + RCLCPP_INFO(logger_, "VoxelBasedApproximateDynamicMapLoader initialized.\n"); + } + bool is_close_to_map(const pcl::PointXYZ & point, const double distance_threshold) override; + }; + + class VoxelBasedApproximateCompareMapFilterComponent + : public autoware::pointcloud_preprocessor::Filter + { + protected: + virtual void filter( + const PointCloud2ConstPtr & input, const IndicesPtr & indices, PointCloud2 & output); + + private: + double distance_threshold_; + std::unique_ptr voxel_based_approximate_map_loader_; + + public: + PCL_MAKE_ALIGNED_OPERATOR_NEW + explicit VoxelBasedApproximateCompareMapFilterComponent(const rclcpp::NodeOptions & options); + }; +} // namespace autoware::compare_map_segmentation + +// clang-format off +#endif // VOXEL_BASED_APPROXIMATE_COMPARE_MAP_FILTER__NODE_HPP_ // NOLINT +// clang-format on diff --git a/perception/compare_map_segmentation/src/voxel_based_compare_map_filter/node.cpp b/perception/compare_map_segmentation/src/voxel_based_compare_map_filter/node.cpp new file mode 100644 index 0000000000000..a3eb866d77e31 --- /dev/null +++ b/perception/compare_map_segmentation/src/voxel_based_compare_map_filter/node.cpp @@ -0,0 +1,116 @@ +// Copyright 2020 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "node.hpp" + +#include "autoware/universe_utils/ros/debug_publisher.hpp" +#include "autoware/universe_utils/system/stop_watch.hpp" + +#include +#include +#include + +#include +#include + +namespace autoware::compare_map_segmentation +{ +using autoware::pointcloud_preprocessor::get_param; + +VoxelBasedCompareMapFilterComponent::VoxelBasedCompareMapFilterComponent( + const rclcpp::NodeOptions & options) +: Filter("VoxelBasedCompareMapFilter", options) +{ + // initialize debug tool + { + using autoware::universe_utils::DebugPublisher; + using autoware::universe_utils::StopWatch; + stop_watch_ptr_ = std::make_unique>(); + debug_publisher_ = std::make_unique(this, "voxel_based_compare_map_filter"); + stop_watch_ptr_->tic("cyclic_time"); + stop_watch_ptr_->tic("processing_time"); + } + + distance_threshold_ = declare_parameter("distance_threshold"); + bool use_dynamic_map_loading = declare_parameter("use_dynamic_map_loading"); + double downsize_ratio_z_axis = declare_parameter("downsize_ratio_z_axis"); + if (downsize_ratio_z_axis <= 0.0) { + RCLCPP_ERROR(this->get_logger(), "downsize_ratio_z_axis should be positive"); + return; + } + set_map_in_voxel_grid_ = false; + if (use_dynamic_map_loading) { + rclcpp::CallbackGroup::SharedPtr main_callback_group; + main_callback_group = this->create_callback_group(rclcpp::CallbackGroupType::MutuallyExclusive); + voxel_grid_map_loader_ = std::make_unique( + this, distance_threshold_, downsize_ratio_z_axis, &tf_input_frame_, &mutex_, + main_callback_group); + } else { + voxel_grid_map_loader_ = std::make_unique( + this, distance_threshold_, downsize_ratio_z_axis, &tf_input_frame_, &mutex_); + } + tf_input_frame_ = *(voxel_grid_map_loader_->tf_map_input_frame_); + RCLCPP_INFO(this->get_logger(), "tf_map_input_frame: %s", tf_input_frame_.c_str()); +} + +void VoxelBasedCompareMapFilterComponent::filter( + const PointCloud2ConstPtr & input, [[maybe_unused]] const IndicesPtr & indices, + PointCloud2 & output) +{ + std::scoped_lock lock(mutex_); + stop_watch_ptr_->toc("processing_time", true); + int point_step = input->point_step; + int offset_x = input->fields[pcl::getFieldIndex(*input, "x")].offset; + int offset_y = input->fields[pcl::getFieldIndex(*input, "y")].offset; + int offset_z = input->fields[pcl::getFieldIndex(*input, "z")].offset; + + output.data.resize(input->data.size()); + output.point_step = point_step; + size_t output_size = 0; + for (size_t global_offset = 0; global_offset < input->data.size(); global_offset += point_step) { + pcl::PointXYZ point{}; + std::memcpy(&point.x, &input->data[global_offset + offset_x], sizeof(float)); + std::memcpy(&point.y, &input->data[global_offset + offset_y], sizeof(float)); + std::memcpy(&point.z, &input->data[global_offset + offset_z], sizeof(float)); + if (voxel_grid_map_loader_->is_close_to_map(point, distance_threshold_)) { + continue; + } + std::memcpy(&output.data[output_size], &input->data[global_offset], point_step); + output_size += point_step; + } + output.header = input->header; + output.fields = input->fields; + output.data.resize(output_size); + output.height = input->height; + output.width = output_size / point_step / output.height; + output.row_step = output_size / output.height; + output.is_bigendian = input->is_bigendian; + output.is_dense = input->is_dense; + + // add processing time for debug + if (debug_publisher_) { + const double cyclic_time_ms = stop_watch_ptr_->toc("cyclic_time", true); + const double processing_time_ms = stop_watch_ptr_->toc("processing_time", true); + debug_publisher_->publish( + "debug/cyclic_time_ms", cyclic_time_ms); + debug_publisher_->publish( + "debug/processing_time_ms", processing_time_ms); + } +} + +} // namespace autoware::compare_map_segmentation + +#include +RCLCPP_COMPONENTS_REGISTER_NODE( + autoware::compare_map_segmentation::VoxelBasedCompareMapFilterComponent) diff --git a/perception/compare_map_segmentation/src/voxel_based_compare_map_filter/node.hpp b/perception/compare_map_segmentation/src/voxel_based_compare_map_filter/node.hpp new file mode 100644 index 0000000000000..db62669c87094 --- /dev/null +++ b/perception/compare_map_segmentation/src/voxel_based_compare_map_filter/node.hpp @@ -0,0 +1,57 @@ +// Copyright 2020 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef VOXEL_BASED_COMPARE_MAP_FILTER__NODE_HPP_ +#define VOXEL_BASED_COMPARE_MAP_FILTER__NODE_HPP_ + +#include "../voxel_grid_map_loader/voxel_grid_map_loader.hpp" +<<<<<<<< + HEAD : perception / compare_map_segmentation / src / voxel_based_compare_map_filter / node.hpp +#include "pointcloud_preprocessor/filter.hpp" + == == == == +#include "autoware/pointcloud_preprocessor/filter.hpp" + >>>>>>>> original / + main : perception / autoware_compare_map_segmentation / src / voxel_based_compare_map_filter / + node.hpp + +#include +#include + +#include +#include + + namespace autoware::compare_map_segmentation +{ + class VoxelBasedCompareMapFilterComponent : public autoware::pointcloud_preprocessor::Filter + { + protected: + virtual void filter( + const PointCloud2ConstPtr & input, const IndicesPtr & indices, PointCloud2 & output); + + private: + // pcl::SegmentDifferences impl_; + std::unique_ptr voxel_grid_map_loader_; + rclcpp::Subscription::SharedPtr sub_map_; + double distance_threshold_; + bool set_map_in_voxel_grid_; + + bool dynamic_map_load_enable_; + + public: + PCL_MAKE_ALIGNED_OPERATOR_NEW + explicit VoxelBasedCompareMapFilterComponent(const rclcpp::NodeOptions & options); + }; +} // namespace autoware::compare_map_segmentation + +#endif // VOXEL_BASED_COMPARE_MAP_FILTER__NODE_HPP_ diff --git a/perception/compare_map_segmentation/src/voxel_distance_based_compare_map_filter/node.cpp b/perception/compare_map_segmentation/src/voxel_distance_based_compare_map_filter/node.cpp new file mode 100644 index 0000000000000..7fdeb5d8ea163 --- /dev/null +++ b/perception/compare_map_segmentation/src/voxel_distance_based_compare_map_filter/node.cpp @@ -0,0 +1,184 @@ +// Copyright 2020 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "node.hpp" + +#include "autoware/universe_utils/ros/debug_publisher.hpp" +#include "autoware/universe_utils/system/stop_watch.hpp" + +#include +#include +#include + +#include + +namespace autoware::compare_map_segmentation +{ + +void VoxelDistanceBasedStaticMapLoader::onMapCallback( + const sensor_msgs::msg::PointCloud2::ConstSharedPtr map) +{ + pcl::PointCloud map_pcl; + pcl::fromROSMsg(*map, map_pcl); + const auto map_pcl_ptr = pcl::make_shared>(map_pcl); + + (*mutex_ptr_).lock(); + map_ptr_ = map_pcl_ptr; + *tf_map_input_frame_ = map_ptr_->header.frame_id; + // voxel + voxel_map_ptr_.reset(new pcl::PointCloud); + voxel_grid_.setLeafSize(voxel_leaf_size_, voxel_leaf_size_, voxel_leaf_size_); + voxel_grid_.setInputCloud(map_pcl_ptr); + voxel_grid_.setSaveLeafLayout(true); + voxel_grid_.filter(*voxel_map_ptr_); + // kdtree + map_ptr_ = map_pcl_ptr; + + if (!tree_) { + if (map_ptr_->isOrganized()) { + tree_.reset(new pcl::search::OrganizedNeighbor()); + } else { + tree_.reset(new pcl::search::KdTree(false)); + } + } + tree_->setInputCloud(map_ptr_); + (*mutex_ptr_).unlock(); +} + +bool VoxelDistanceBasedStaticMapLoader::is_close_to_map( + const pcl::PointXYZ & point, const double distance_threshold) +{ + if (voxel_map_ptr_ == NULL) { + return false; + } + if (map_ptr_ == NULL) { + return false; + } + if (tree_ == NULL) { + return false; + } + if (is_close_to_neighbor_voxels(point, distance_threshold, voxel_grid_, tree_)) { + return true; + } + return false; +} + +bool VoxelDistanceBasedDynamicMapLoader::is_close_to_map( + const pcl::PointXYZ & point, const double distance_threshold) +{ + if (current_voxel_grid_dict_.size() == 0) { + return false; + } + + const int map_grid_index = static_cast( + std::floor((point.x - origin_x_) / map_grid_size_x_) + + map_grids_x_ * std::floor((point.y - origin_y_) / map_grid_size_y_)); + + if (static_cast(map_grid_index) >= current_voxel_grid_array_.size()) { + return false; + } + if ( + current_voxel_grid_array_.at(map_grid_index) != NULL && + is_close_to_neighbor_voxels( + point, distance_threshold, current_voxel_grid_array_.at(map_grid_index)->map_cell_voxel_grid, + current_voxel_grid_array_.at(map_grid_index)->map_cell_kdtree)) { + return true; + } + return false; +} + +VoxelDistanceBasedCompareMapFilterComponent::VoxelDistanceBasedCompareMapFilterComponent( + const rclcpp::NodeOptions & options) +: Filter("VoxelDistanceBasedCompareMapFilter", options) +{ + // initialize debug tool + { + using autoware::universe_utils::DebugPublisher; + using autoware::universe_utils::StopWatch; + stop_watch_ptr_ = std::make_unique>(); + debug_publisher_ = + std::make_unique(this, "voxel_distance_based_compare_map_filter"); + stop_watch_ptr_->tic("cyclic_time"); + stop_watch_ptr_->tic("processing_time"); + } + + distance_threshold_ = declare_parameter("distance_threshold"); + bool use_dynamic_map_loading = declare_parameter("use_dynamic_map_loading"); + double downsize_ratio_z_axis = declare_parameter("downsize_ratio_z_axis"); + if (downsize_ratio_z_axis <= 0.0) { + RCLCPP_ERROR(this->get_logger(), "downsize_ratio_z_axis should be positive"); + return; + } + if (use_dynamic_map_loading) { + rclcpp::CallbackGroup::SharedPtr main_callback_group; + main_callback_group = this->create_callback_group(rclcpp::CallbackGroupType::MutuallyExclusive); + voxel_distance_based_map_loader_ = std::make_unique( + this, distance_threshold_, downsize_ratio_z_axis, &tf_input_frame_, &mutex_, + main_callback_group); + } else { + voxel_distance_based_map_loader_ = std::make_unique( + this, distance_threshold_, downsize_ratio_z_axis, &tf_input_frame_, &mutex_); + } +} + +void VoxelDistanceBasedCompareMapFilterComponent::filter( + const PointCloud2ConstPtr & input, [[maybe_unused]] const IndicesPtr & indices, + PointCloud2 & output) +{ + std::scoped_lock lock(mutex_); + stop_watch_ptr_->toc("processing_time", true); + int point_step = input->point_step; + int offset_x = input->fields[pcl::getFieldIndex(*input, "x")].offset; + int offset_y = input->fields[pcl::getFieldIndex(*input, "y")].offset; + int offset_z = input->fields[pcl::getFieldIndex(*input, "z")].offset; + + output.data.resize(input->data.size()); + output.point_step = point_step; + size_t output_size = 0; + for (size_t global_offset = 0; global_offset < input->data.size(); global_offset += point_step) { + pcl::PointXYZ point{}; + std::memcpy(&point.x, &input->data[global_offset + offset_x], sizeof(float)); + std::memcpy(&point.y, &input->data[global_offset + offset_y], sizeof(float)); + std::memcpy(&point.z, &input->data[global_offset + offset_z], sizeof(float)); + if (voxel_distance_based_map_loader_->is_close_to_map(point, distance_threshold_)) { + continue; + } + std::memcpy(&output.data[output_size], &input->data[global_offset], point_step); + output_size += point_step; + } + + output.header = input->header; + output.fields = input->fields; + output.data.resize(output_size); + output.height = input->height; + output.width = output_size / point_step / output.height; + output.row_step = output_size / output.height; + output.is_bigendian = input->is_bigendian; + output.is_dense = input->is_dense; + + // add processing time for debug + if (debug_publisher_) { + const double cyclic_time_ms = stop_watch_ptr_->toc("cyclic_time", true); + const double processing_time_ms = stop_watch_ptr_->toc("processing_time", true); + debug_publisher_->publish( + "debug/cyclic_time_ms", cyclic_time_ms); + debug_publisher_->publish( + "debug/processing_time_ms", processing_time_ms); + } +} +} // namespace autoware::compare_map_segmentation + +#include +RCLCPP_COMPONENTS_REGISTER_NODE( + autoware::compare_map_segmentation::VoxelDistanceBasedCompareMapFilterComponent) diff --git a/perception/compare_map_segmentation/src/voxel_distance_based_compare_map_filter/node.hpp b/perception/compare_map_segmentation/src/voxel_distance_based_compare_map_filter/node.hpp new file mode 100644 index 0000000000000..2199e5ccf476d --- /dev/null +++ b/perception/compare_map_segmentation/src/voxel_distance_based_compare_map_filter/node.hpp @@ -0,0 +1,155 @@ +// Copyright 2020 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef VOXEL_DISTANCE_BASED_COMPARE_MAP_FILTER__NODE_HPP_ // NOLINT +#define VOXEL_DISTANCE_BASED_COMPARE_MAP_FILTER__NODE_HPP_ // NOLINT + +#include "../voxel_grid_map_loader/voxel_grid_map_loader.hpp" +<<<<<<<< + HEAD : perception / compare_map_segmentation / src / voxel_distance_based_compare_map_filter / + node.hpp +#include "pointcloud_preprocessor/filter.hpp" + == == == == +#include "autoware/pointcloud_preprocessor/filter.hpp" + >>>>>>>> original / + main : perception / autoware_compare_map_segmentation / src / + voxel_distance_based_compare_map_filter / + node.hpp + +#include +#include + +#include +#include +#include +#include +#include + + namespace autoware::compare_map_segmentation +{ + typedef typename pcl::Filter::PointCloud PointCloud; + typedef typename PointCloud::Ptr PointCloudPtr; + typedef typename PointCloud::ConstPtr PointCloudConstPtr; + + class VoxelDistanceBasedStaticMapLoader : public VoxelGridStaticMapLoader + { + private: + PointCloudConstPtr map_ptr_; + pcl::search::Search::Ptr tree_; + + public: + explicit VoxelDistanceBasedStaticMapLoader( + rclcpp::Node * node, double leaf_size, double downsize_ratio_z_axis, + std::string * tf_map_input_frame, std::mutex * mutex) + : VoxelGridStaticMapLoader(node, leaf_size, downsize_ratio_z_axis, tf_map_input_frame, mutex) + { + RCLCPP_INFO(logger_, "VoxelDistanceBasedStaticMapLoader initialized.\n"); + } + bool is_close_to_map(const pcl::PointXYZ & point, const double distance_threshold) override; + void onMapCallback(const sensor_msgs::msg::PointCloud2::ConstSharedPtr map) override; + }; + + class VoxelDistanceBasedDynamicMapLoader : public VoxelGridDynamicMapLoader + { + protected: + private: + PointCloudConstPtr map_ptr_; + /* data */ + public: + explicit VoxelDistanceBasedDynamicMapLoader( + rclcpp::Node * node, double leaf_size, double downsize_ratio_z_axis, + std::string * tf_map_input_frame, std::mutex * mutex, + rclcpp::CallbackGroup::SharedPtr main_callback_group) + : VoxelGridDynamicMapLoader( + node, leaf_size, downsize_ratio_z_axis, tf_map_input_frame, mutex, main_callback_group) + { + RCLCPP_INFO(logger_, "VoxelDistanceBasedDynamicMapLoader initialized.\n"); + } + bool is_close_to_map(const pcl::PointXYZ & point, const double distance_threshold) override; + + inline void addMapCellAndFilter( + const autoware_map_msgs::msg::PointCloudMapCellWithID & map_cell_to_add) override + { + map_grid_size_x_ = map_cell_to_add.metadata.max_x - map_cell_to_add.metadata.min_x; + map_grid_size_y_ = map_cell_to_add.metadata.max_y - map_cell_to_add.metadata.min_y; + + pcl::PointCloud map_cell_pc_tmp; + pcl::fromROSMsg(map_cell_to_add.pointcloud, map_cell_pc_tmp); + + VoxelGridPointXYZ map_cell_voxel_grid_tmp; + PointCloudPtr map_cell_downsampled_pc_ptr_tmp; + + auto map_cell_voxel_input_tmp_ptr = + std::make_shared>(map_cell_pc_tmp); + map_cell_voxel_grid_tmp.setLeafSize(voxel_leaf_size_, voxel_leaf_size_, voxel_leaf_size_); + map_cell_downsampled_pc_ptr_tmp.reset(new pcl::PointCloud); + map_cell_voxel_grid_tmp.setInputCloud(map_cell_voxel_input_tmp_ptr); + map_cell_voxel_grid_tmp.setSaveLeafLayout(true); + map_cell_voxel_grid_tmp.filter(*map_cell_downsampled_pc_ptr_tmp); + + MapGridVoxelInfo current_voxel_grid_list_item; + current_voxel_grid_list_item.min_b_x = map_cell_to_add.metadata.min_x; + current_voxel_grid_list_item.min_b_y = map_cell_to_add.metadata.min_y; + current_voxel_grid_list_item.max_b_x = map_cell_to_add.metadata.max_x; + current_voxel_grid_list_item.max_b_y = map_cell_to_add.metadata.max_y; + + current_voxel_grid_list_item.map_cell_voxel_grid.set_voxel_grid( + &(map_cell_voxel_grid_tmp.leaf_layout_), map_cell_voxel_grid_tmp.get_min_b(), + map_cell_voxel_grid_tmp.get_max_b(), map_cell_voxel_grid_tmp.get_div_b(), + map_cell_voxel_grid_tmp.get_divb_mul(), map_cell_voxel_grid_tmp.get_inverse_leaf_size()); + + current_voxel_grid_list_item.map_cell_pc_ptr.reset(new pcl::PointCloud); + current_voxel_grid_list_item.map_cell_pc_ptr = std::move(map_cell_downsampled_pc_ptr_tmp); + + // add kdtree + pcl::search::Search::Ptr tree_tmp; + if (!tree_tmp) { + if (map_cell_voxel_input_tmp_ptr->isOrganized()) { + tree_tmp.reset(new pcl::search::OrganizedNeighbor()); + } else { + tree_tmp.reset(new pcl::search::KdTree(false)); + } + } + tree_tmp->setInputCloud(map_cell_voxel_input_tmp_ptr); + current_voxel_grid_list_item.map_cell_kdtree = tree_tmp; + + // add + (*mutex_ptr_).lock(); + current_voxel_grid_dict_.insert({map_cell_to_add.cell_id, current_voxel_grid_list_item}); + (*mutex_ptr_).unlock(); + } + }; + + class VoxelDistanceBasedCompareMapFilterComponent + : public autoware::pointcloud_preprocessor::Filter + { + protected: + virtual void filter( + const PointCloud2ConstPtr & input, const IndicesPtr & indices, PointCloud2 & output); + + void input_target_callback(const PointCloud2ConstPtr map); + + private: + std::unique_ptr voxel_distance_based_map_loader_; + double distance_threshold_; + + public: + PCL_MAKE_ALIGNED_OPERATOR_NEW + explicit VoxelDistanceBasedCompareMapFilterComponent(const rclcpp::NodeOptions & options); + }; +} // namespace autoware::compare_map_segmentation + +// clang-format off +#endif // VOXEL_DISTANCE_BASED_COMPARE_MAP_FILTER__NODE_HPP_ // NOLINT +// clang-format on diff --git a/perception/compare_map_segmentation/src/voxel_grid_map_loader/voxel_grid_map_loader.cpp b/perception/compare_map_segmentation/src/voxel_grid_map_loader/voxel_grid_map_loader.cpp new file mode 100644 index 0000000000000..b011799e5b912 --- /dev/null +++ b/perception/compare_map_segmentation/src/voxel_grid_map_loader/voxel_grid_map_loader.cpp @@ -0,0 +1,468 @@ +// Copyright 2023 Autoware Foundation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "voxel_grid_map_loader.hpp" + +namespace autoware::compare_map_segmentation +{ +VoxelGridMapLoader::VoxelGridMapLoader( + rclcpp::Node * node, double leaf_size, double downsize_ratio_z_axis, + std::string * tf_map_input_frame, std::mutex * mutex) +: logger_(node->get_logger()), + voxel_leaf_size_(leaf_size), + downsize_ratio_z_axis_(downsize_ratio_z_axis) +{ + tf_map_input_frame_ = tf_map_input_frame; + mutex_ptr_ = mutex; + + downsampled_map_pub_ = node->create_publisher( + "debug/downsampled_map/pointcloud", rclcpp::QoS{1}.transient_local()); + debug_ = node->declare_parameter("publish_debug_pcd"); +} + +bool VoxelGridMapLoader::is_close_points( + const pcl::PointXYZ point, const pcl::PointXYZ target_point, + const double distance_threshold) const +{ + if (distance3D(point, target_point) < distance_threshold * distance_threshold) { + return true; + } + return false; +} + +void VoxelGridMapLoader::publish_downsampled_map( + const pcl::PointCloud & downsampled_pc) +{ + sensor_msgs::msg::PointCloud2 downsampled_map_msg; + pcl::toROSMsg(downsampled_pc, downsampled_map_msg); + downsampled_map_msg.header.frame_id = "map"; + downsampled_map_pub_->publish(downsampled_map_msg); +} + +bool VoxelGridMapLoader::is_close_to_neighbor_voxels( + const pcl::PointXYZ & point, const double distance_threshold, VoxelGridPointXYZ & voxel, + pcl::search::Search::Ptr tree) const +{ + const int index = voxel.getCentroidIndexAt(voxel.getGridCoordinates(point.x, point.y, point.z)); + if (index != -1) { + return true; + } + if (tree == NULL) { + return false; + } + std::vector nn_indices(1); + std::vector nn_distances(1); + if (tree->radiusSearch(point, distance_threshold, nn_indices, nn_distances, 1) == 0) { + return false; + } + return true; +} + +bool VoxelGridMapLoader::is_close_to_neighbor_voxels( + const pcl::PointXYZ & point, const double distance_threshold, const PointCloudPtr & map, + VoxelGridPointXYZ & voxel) const +{ + // check map downsampled pc + double distance_threshold_z = downsize_ratio_z_axis_ * distance_threshold; + if (map == NULL) { + return false; + } + if (is_in_voxel( + pcl::PointXYZ(point.x, point.y, point.z), point, distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x, point.y - distance_threshold, point.z - distance_threshold_z), point, + distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x, point.y - distance_threshold, point.z), point, distance_threshold, + map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x, point.y - distance_threshold, point.z + distance_threshold_z), point, + distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x, point.y, point.z - distance_threshold_z), point, distance_threshold, + map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x, point.y, point.z + distance_threshold_z), point, distance_threshold, + map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x, point.y + distance_threshold, point.z - distance_threshold_z), point, + distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x, point.y + distance_threshold, point.z), point, distance_threshold, + map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x, point.y + distance_threshold, point.z + distance_threshold_z), point, + distance_threshold, map, voxel)) { + return true; + } + + if (is_in_voxel( + pcl::PointXYZ( + point.x - distance_threshold, point.y - distance_threshold, + point.z - distance_threshold_z), + point, distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x - distance_threshold, point.y - distance_threshold, point.z), point, + distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ( + point.x - distance_threshold, point.y - distance_threshold, + point.z + distance_threshold_z), + point, distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x - distance_threshold, point.y, point.z - distance_threshold_z), point, + distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x - distance_threshold, point.y, point.z), point, distance_threshold, + map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x - distance_threshold, point.y, point.z + distance_threshold_z), point, + distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ( + point.x - distance_threshold, point.y + distance_threshold, + point.z - distance_threshold_z), + point, distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x - distance_threshold, point.y + distance_threshold, point.z), point, + distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ( + point.x - distance_threshold, point.y + distance_threshold, + point.z + distance_threshold_z), + point, distance_threshold, map, voxel)) { + return true; + } + + if (is_in_voxel( + pcl::PointXYZ( + point.x + distance_threshold, point.y - distance_threshold, + point.z - distance_threshold_z), + point, distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x + distance_threshold, point.y - distance_threshold, point.z), point, + distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ( + point.x + distance_threshold, point.y - distance_threshold, + point.z + distance_threshold_z), + point, distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x + distance_threshold, point.y, point.z - distance_threshold_z), point, + distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x + distance_threshold, point.y, point.z), point, distance_threshold, + map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x + distance_threshold, point.y, point.z + distance_threshold_z), point, + distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ( + point.x + distance_threshold, point.y + distance_threshold, + point.z - distance_threshold_z), + point, distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ(point.x + distance_threshold, point.y + distance_threshold, point.z), point, + distance_threshold, map, voxel)) { + return true; + } + if (is_in_voxel( + pcl::PointXYZ( + point.x + distance_threshold, point.y + distance_threshold, + point.z + distance_threshold_z), + point, distance_threshold, map, voxel)) { + return true; + } + return false; +} + +bool VoxelGridMapLoader::is_in_voxel( + const pcl::PointXYZ & src_point, const pcl::PointXYZ & target_point, + const double distance_threshold, const PointCloudPtr & map, VoxelGridPointXYZ & voxel) const +{ + int voxel_index = + voxel.getCentroidIndexAt(voxel.getGridCoordinates(src_point.x, src_point.y, src_point.z)); + if (voxel_index != -1) { // not empty voxel + const double dist_x = map->points.at(voxel_index).x - target_point.x; + const double dist_y = map->points.at(voxel_index).y - target_point.y; + const double dist_z = map->points.at(voxel_index).z - target_point.z; + // check if the point is inside the distance threshold voxel + if ( + std::abs(dist_x) < distance_threshold && std::abs(dist_y) < distance_threshold && + std::abs(dist_z) < distance_threshold * downsize_ratio_z_axis_) { + return true; + } + } + return false; +} + +VoxelGridStaticMapLoader::VoxelGridStaticMapLoader( + rclcpp::Node * node, double leaf_size, double downsize_ratio_z_axis, + std::string * tf_map_input_frame, std::mutex * mutex) +: VoxelGridMapLoader(node, leaf_size, downsize_ratio_z_axis, tf_map_input_frame, mutex) +{ + voxel_leaf_size_z_ = voxel_leaf_size_ * downsize_ratio_z_axis_; + sub_map_ = node->create_subscription( + "map", rclcpp::QoS{1}.transient_local(), + std::bind(&VoxelGridStaticMapLoader::onMapCallback, this, std::placeholders::_1)); + RCLCPP_INFO(logger_, "VoxelGridStaticMapLoader initialized.\n"); +} + +void VoxelGridStaticMapLoader::onMapCallback( + const sensor_msgs::msg::PointCloud2::ConstSharedPtr map) +{ + pcl::PointCloud map_pcl; + pcl::fromROSMsg(*map, map_pcl); + const auto map_pcl_ptr = pcl::make_shared>(map_pcl); + *tf_map_input_frame_ = map_pcl_ptr->header.frame_id; + (*mutex_ptr_).lock(); + voxel_map_ptr_.reset(new pcl::PointCloud); + voxel_grid_.setLeafSize(voxel_leaf_size_, voxel_leaf_size_, voxel_leaf_size_z_); + voxel_grid_.setInputCloud(map_pcl_ptr); + voxel_grid_.setSaveLeafLayout(true); + voxel_grid_.filter(*voxel_map_ptr_); + (*mutex_ptr_).unlock(); + + if (debug_) { + publish_downsampled_map(*voxel_map_ptr_); + } +} +bool VoxelGridStaticMapLoader::is_close_to_map( + const pcl::PointXYZ & point, const double distance_threshold) +{ + if (is_close_to_neighbor_voxels(point, distance_threshold, voxel_map_ptr_, voxel_grid_)) { + return true; + } + return false; +} + +VoxelGridDynamicMapLoader::VoxelGridDynamicMapLoader( + rclcpp::Node * node, double leaf_size, double downsize_ratio_z_axis, + std::string * tf_map_input_frame, std::mutex * mutex, + rclcpp::CallbackGroup::SharedPtr main_callback_group) +: VoxelGridMapLoader(node, leaf_size, downsize_ratio_z_axis, tf_map_input_frame, mutex) +{ + voxel_leaf_size_z_ = voxel_leaf_size_ * downsize_ratio_z_axis_; + auto timer_interval_ms = node->declare_parameter("timer_interval_ms"); + map_update_distance_threshold_ = node->declare_parameter("map_update_distance_threshold"); + map_loader_radius_ = node->declare_parameter("map_loader_radius"); + auto main_sub_opt = rclcpp::SubscriptionOptions(); + main_sub_opt.callback_group = main_callback_group; + sub_kinematic_state_ = node->create_subscription( + "kinematic_state", rclcpp::QoS{1}, + std::bind(&VoxelGridDynamicMapLoader::onEstimatedPoseCallback, this, std::placeholders::_1), + main_sub_opt); + RCLCPP_INFO(logger_, "VoxelGridDynamicMapLoader initialized.\n"); + + client_callback_group_ = + node->create_callback_group(rclcpp::CallbackGroupType::MutuallyExclusive); + map_update_client_ = node->create_client( + "map_loader_service", rmw_qos_profile_services_default, client_callback_group_); + + while (!map_update_client_->wait_for_service(std::chrono::seconds(1)) && rclcpp::ok()) { + RCLCPP_INFO(logger_, "service not available, waiting again ..."); + } + + const auto period_ns = rclcpp::Rate(timer_interval_ms).period(); + timer_callback_group_ = node->create_callback_group(rclcpp::CallbackGroupType::MutuallyExclusive); + map_update_timer_ = rclcpp::create_timer( + node, node->get_clock(), period_ns, std::bind(&VoxelGridDynamicMapLoader::timer_callback, this), + timer_callback_group_); +} +void VoxelGridDynamicMapLoader::onEstimatedPoseCallback(nav_msgs::msg::Odometry::ConstSharedPtr msg) +{ + current_position_ = msg->pose.pose.position; +} +bool VoxelGridDynamicMapLoader::is_close_to_next_map_grid( + const pcl::PointXYZ & point, const int current_map_grid_index, const double distance_threshold) +{ + int neighbor_map_grid_index = static_cast( + std::floor((point.x - origin_x_) / map_grid_size_x_) + + map_grids_x_ * std::floor((point.y - origin_y_) / map_grid_size_y_)); + + if ( + static_cast(neighbor_map_grid_index) >= current_voxel_grid_array_.size() || + neighbor_map_grid_index == current_map_grid_index || + current_voxel_grid_array_.at(neighbor_map_grid_index) != NULL) { + return false; + } + if (is_close_to_neighbor_voxels( + point, distance_threshold, + current_voxel_grid_array_.at(neighbor_map_grid_index)->map_cell_pc_ptr, + current_voxel_grid_array_.at(neighbor_map_grid_index)->map_cell_voxel_grid)) { + return true; + } + return false; +} + +bool VoxelGridDynamicMapLoader::is_close_to_map( + const pcl::PointXYZ & point, const double distance_threshold) +{ + if (current_voxel_grid_dict_.size() == 0) { + return false; + } + + // Compare point with map grid that point belong to + + int map_grid_index = static_cast( + std::floor((point.x - origin_x_) / map_grid_size_x_) + + map_grids_x_ * std::floor((point.y - origin_y_) / map_grid_size_y_)); + + if (static_cast(map_grid_index) >= current_voxel_grid_array_.size()) { + return false; + } + if ( + current_voxel_grid_array_.at(map_grid_index) != NULL && + is_close_to_neighbor_voxels( + point, distance_threshold, current_voxel_grid_array_.at(map_grid_index)->map_cell_pc_ptr, + current_voxel_grid_array_.at(map_grid_index)->map_cell_voxel_grid)) { + return true; + } + + // Compare point with the neighbor map cells if point close to map cell boundary + + if (is_close_to_next_map_grid( + pcl::PointXYZ(point.x - distance_threshold, point.y, point.z), map_grid_index, + distance_threshold)) { + return true; + } + + if (is_close_to_next_map_grid( + pcl::PointXYZ(point.x + distance_threshold, point.y, point.z), map_grid_index, + distance_threshold)) { + return true; + } + + if (is_close_to_next_map_grid( + pcl::PointXYZ(point.x, point.y - distance_threshold, point.z), map_grid_index, + distance_threshold)) { + return true; + } + if (is_close_to_next_map_grid( + pcl::PointXYZ(point.x, point.y + distance_threshold, point.z), map_grid_index, + distance_threshold)) { + return true; + } + + return false; +} +void VoxelGridDynamicMapLoader::timer_callback() +{ + if (current_position_ == std::nullopt) { + return; + } + if (last_updated_position_ == std::nullopt) { + request_update_map(current_position_.value()); + last_updated_position_ = current_position_; + return; + } + + if (should_update_map()) { + last_updated_position_ = current_position_; + request_update_map((current_position_.value())); + last_updated_position_ = current_position_; + } +} + +bool VoxelGridDynamicMapLoader::should_update_map() const +{ + if ( + distance2D(current_position_.value(), last_updated_position_.value()) > + map_update_distance_threshold_) { + return true; + } + return false; +} + +void VoxelGridDynamicMapLoader::request_update_map(const geometry_msgs::msg::Point & position) +{ + auto request = std::make_shared(); + request->area.center_x = position.x; + request->area.center_y = position.y; + request->area.radius = map_loader_radius_; + request->cached_ids = getCurrentMapIDs(); + + auto result{map_update_client_->async_send_request( + request, + [](rclcpp::Client::SharedFuture) {})}; + + std::future_status status = result.wait_for(std::chrono::seconds(0)); + while (status != std::future_status::ready) { + RCLCPP_INFO(logger_, "Waiting for response...\n"); + if (!rclcpp::ok()) { + return; + } + status = result.wait_for(std::chrono::seconds(1)); + } + // + if (status == std::future_status::ready) { + if ( + result.get()->new_pointcloud_with_ids.size() == 0 && + result.get()->ids_to_remove.size() == 0) { + return; + } + updateDifferentialMapCells(result.get()->new_pointcloud_with_ids, result.get()->ids_to_remove); + if (debug_) { + publish_downsampled_map(getCurrentDownsampledMapPc()); + } + } +} + +} // namespace autoware::compare_map_segmentation diff --git a/perception/compare_map_segmentation/src/voxel_grid_map_loader/voxel_grid_map_loader.hpp b/perception/compare_map_segmentation/src/voxel_grid_map_loader/voxel_grid_map_loader.hpp new file mode 100644 index 0000000000000..b13f2e537ee55 --- /dev/null +++ b/perception/compare_map_segmentation/src/voxel_grid_map_loader/voxel_grid_map_loader.hpp @@ -0,0 +1,326 @@ +// Copyright 2023 Autoware Foundation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef VOXEL_GRID_MAP_LOADER__VOXEL_GRID_MAP_LOADER_HPP_ +#define VOXEL_GRID_MAP_LOADER__VOXEL_GRID_MAP_LOADER_HPP_ + +#include + +#include "autoware_map_msgs/srv/get_differential_point_cloud_map.hpp" +#include +#include +#include + +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +namespace autoware::compare_map_segmentation +{ +template +double distance3D(const T p1, const U p2) +{ + double dx = p1.x - p2.x; + double dy = p1.y - p2.y; + double dz = p1.z - p2.z; + return dx * dx + dy * dy + dz * dz; +} + +template +double distance2D(const T p1, const U p2) +{ + double dx = p1.x - p2.x; + double dy = p1.y - p2.y; + return std::sqrt(dx * dx + dy * dy); +} + +template +class VoxelGridEx : public pcl::VoxelGrid +{ +protected: + using pcl::VoxelGrid::save_leaf_layout_; + using pcl::VoxelGrid::min_b_; + using pcl::VoxelGrid::max_b_; + using pcl::VoxelGrid::divb_mul_; + using pcl::VoxelGrid::div_b_; + using pcl::VoxelGrid::inverse_leaf_size_; + + using PointCloud = typename pcl::Filter::PointCloud; + using PointCloudPtr = typename PointCloud::Ptr; + using PointCloudConstPtr = typename PointCloud::ConstPtr; + +public: + using pcl::VoxelGrid::leaf_layout_; + + inline void set_voxel_grid( + std::vector * leaf_layout, const Eigen::Vector4i & min_b, const Eigen::Vector4i & max_b, + const Eigen::Vector4i & div_b, const Eigen::Vector4i & divb_mul, + const Eigen::Array4f & inverse_leaf_size) + { + leaf_layout_ = std::move(*leaf_layout); + min_b_ = min_b; + max_b_ = max_b; + div_b_ = div_b; + divb_mul_ = divb_mul; + inverse_leaf_size_ = inverse_leaf_size; + } + + inline Eigen::Vector4i get_min_b() const { return min_b_; } + inline Eigen::Vector4i get_divb_mul() const { return divb_mul_; } + inline Eigen::Vector4i get_max_b() const { return max_b_; } + inline Eigen::Vector4i get_div_b() const { return div_b_; } + inline Eigen::Array4f get_inverse_leaf_size() const { return inverse_leaf_size_; } + inline std::vector getLeafLayout() { return (leaf_layout_); } +}; + +class VoxelGridMapLoader +{ +protected: + rclcpp::Logger logger_; + std::mutex * mutex_ptr_; + double voxel_leaf_size_; + double voxel_leaf_size_z_; + double downsize_ratio_z_axis_; + rclcpp::Publisher::SharedPtr downsampled_map_pub_; + bool debug_ = false; + +public: + typedef VoxelGridEx VoxelGridPointXYZ; + typedef typename pcl::Filter::PointCloud PointCloud; + typedef typename PointCloud::Ptr PointCloudPtr; + explicit VoxelGridMapLoader( + rclcpp::Node * node, double leaf_size, double downsize_ratio_z_axis, + std::string * tf_map_input_frame, std::mutex * mutex); + + virtual bool is_close_to_map(const pcl::PointXYZ & point, const double distance_threshold) = 0; + bool is_close_to_neighbor_voxels( + const pcl::PointXYZ & point, const double distance_threshold, VoxelGridPointXYZ & voxel, + pcl::search::Search::Ptr tree) const; + bool is_close_to_neighbor_voxels( + const pcl::PointXYZ & point, const double distance_threshold, const PointCloudPtr & map, + VoxelGridPointXYZ & voxel) const; + bool is_in_voxel( + const pcl::PointXYZ & src_point, const pcl::PointXYZ & target_point, + const double distance_threshold, const PointCloudPtr & map, VoxelGridPointXYZ & voxel) const; + + void publish_downsampled_map(const pcl::PointCloud & downsampled_pc); + bool is_close_points( + const pcl::PointXYZ point, const pcl::PointXYZ target_point, + const double distance_threshold) const; + std::string * tf_map_input_frame_; +}; + +class VoxelGridStaticMapLoader : public VoxelGridMapLoader +{ +protected: + rclcpp::Subscription::SharedPtr sub_map_; + VoxelGridPointXYZ voxel_grid_; + PointCloudPtr voxel_map_ptr_; + +public: + explicit VoxelGridStaticMapLoader( + rclcpp::Node * node, double leaf_size, double downsize_ratio_z_axis, + std::string * tf_map_input_frame, std::mutex * mutex); + virtual void onMapCallback(const sensor_msgs::msg::PointCloud2::ConstSharedPtr map); + bool is_close_to_map(const pcl::PointXYZ & point, const double distance_threshold) override; +}; + +class VoxelGridDynamicMapLoader : public VoxelGridMapLoader +{ +protected: + struct MapGridVoxelInfo + { + VoxelGridPointXYZ map_cell_voxel_grid; + PointCloudPtr map_cell_pc_ptr; + float min_b_x, min_b_y, max_b_x, max_b_y; + pcl::search::Search::Ptr map_cell_kdtree; + }; + + typedef typename std::map VoxelGridDict; + + /** \brief Map to hold loaded map grid id and it's voxel filter */ + VoxelGridDict current_voxel_grid_dict_; + rclcpp::Subscription::SharedPtr sub_kinematic_state_; + + std::optional current_position_ = std::nullopt; + std::optional last_updated_position_ = std::nullopt; + rclcpp::TimerBase::SharedPtr map_update_timer_; + double map_update_distance_threshold_; + double map_loader_radius_; + rclcpp::Client::SharedPtr + map_update_client_; + rclcpp::CallbackGroup::SharedPtr client_callback_group_; + rclcpp::CallbackGroup::SharedPtr timer_callback_group_; + + /** Map grid size. It might be defined by using metadata */ + double map_grid_size_x_ = -1.0; + double map_grid_size_y_ = -1.0; + + double origin_x_remainder_ = 0.0; + double origin_y_remainder_ = 0.0; + + /** \brief Array to hold loaded map grid positions for fast map grid searching. + */ + std::vector> current_voxel_grid_array_; + + /** \brief Array size in x axis */ + int map_grids_x_; + /** \brief Array size in y axis */ + int map_grids_y_; + + /** \brief x-coordinate of map grid which should belong to array[0][0] */ + float origin_x_; + /** \brief y-coordinate of map grid which should belong to array[0][0] */ + float origin_y_; + +public: + explicit VoxelGridDynamicMapLoader( + rclcpp::Node * node, double leaf_size, double downsize_ratio_z_axis, + std::string * tf_map_input_frame, std::mutex * mutex, + rclcpp::CallbackGroup::SharedPtr main_callback_group); + void onEstimatedPoseCallback(nav_msgs::msg::Odometry::ConstSharedPtr msg); + + void timer_callback(); + bool should_update_map() const; + void request_update_map(const geometry_msgs::msg::Point & position); + bool is_close_to_map(const pcl::PointXYZ & point, const double distance_threshold) override; + /** \brief Check if point close to map pointcloud in the */ + bool is_close_to_next_map_grid( + const pcl::PointXYZ & point, const int current_map_grid_index, const double distance_threshold); + + inline pcl::PointCloud getCurrentDownsampledMapPc() const + { + pcl::PointCloud output; + for (const auto & kv : current_voxel_grid_dict_) { + output = output + *(kv.second.map_cell_pc_ptr); + } + return output; + } + inline std::vector getCurrentMapIDs() const + { + std::vector current_map_ids{}; + for (auto & kv : current_voxel_grid_dict_) { + current_map_ids.push_back(kv.first); + } + return current_map_ids; + } + inline void updateDifferentialMapCells( + const std::vector & map_cells_to_add, + std::vector map_cell_ids_to_remove) + { + for (const auto & map_cell_to_add : map_cells_to_add) { + addMapCellAndFilter(map_cell_to_add); + } + for (size_t i = 0; i < map_cell_ids_to_remove.size(); ++i) { + removeMapCell(map_cell_ids_to_remove.at(i)); + } + + updateVoxelGridArray(); + } + + /** Update loaded map grid array for fast searching*/ + virtual inline void updateVoxelGridArray() + { + origin_x_ = std::floor((current_position_.value().x - map_loader_radius_) / map_grid_size_x_) * + map_grid_size_x_ + + origin_x_remainder_; + origin_y_ = std::floor((current_position_.value().y - map_loader_radius_) / map_grid_size_y_) * + map_grid_size_y_ + + origin_y_remainder_; + + map_grids_x_ = static_cast( + std::ceil((current_position_.value().x + map_loader_radius_ - origin_x_) / map_grid_size_x_)); + map_grids_y_ = static_cast( + std::ceil((current_position_.value().y + map_loader_radius_ - origin_y_) / map_grid_size_y_)); + + if (map_grids_x_ * map_grids_y_ == 0) { + return; + } + + (*mutex_ptr_).lock(); + current_voxel_grid_array_.assign( + map_grids_x_ * map_grid_size_y_, std::make_shared()); + for (const auto & kv : current_voxel_grid_dict_) { + int index = static_cast( + std::floor((kv.second.min_b_x - origin_x_) / map_grid_size_x_) + + map_grids_x_ * std::floor((kv.second.min_b_y - origin_y_) / map_grid_size_y_)); + // TODO(1222-takeshi): check if index is valid + if (index >= map_grids_x_ * map_grids_y_ || index < 0) { + continue; + } + current_voxel_grid_array_.at(index) = std::make_shared(kv.second); + } + (*mutex_ptr_).unlock(); + } + + inline void removeMapCell(const std::string map_cell_id_to_remove) + { + (*mutex_ptr_).lock(); + current_voxel_grid_dict_.erase(map_cell_id_to_remove); + (*mutex_ptr_).unlock(); + } + + virtual inline void addMapCellAndFilter( + const autoware_map_msgs::msg::PointCloudMapCellWithID & map_cell_to_add) + { + map_grid_size_x_ = map_cell_to_add.metadata.max_x - map_cell_to_add.metadata.min_x; + map_grid_size_y_ = map_cell_to_add.metadata.max_y - map_cell_to_add.metadata.min_y; + + origin_x_remainder_ = std::remainder(map_cell_to_add.metadata.min_x, map_grid_size_x_); + origin_y_remainder_ = std::remainder(map_cell_to_add.metadata.min_y, map_grid_size_y_); + + pcl::PointCloud map_cell_pc_tmp; + pcl::fromROSMsg(map_cell_to_add.pointcloud, map_cell_pc_tmp); + + VoxelGridPointXYZ map_cell_voxel_grid_tmp; + PointCloudPtr map_cell_downsampled_pc_ptr_tmp; + + auto map_cell_voxel_input_tmp_ptr = + std::make_shared>(map_cell_pc_tmp); + map_cell_voxel_grid_tmp.setLeafSize(voxel_leaf_size_, voxel_leaf_size_, voxel_leaf_size_z_); + map_cell_downsampled_pc_ptr_tmp.reset(new pcl::PointCloud); + map_cell_voxel_grid_tmp.setInputCloud(map_cell_voxel_input_tmp_ptr); + map_cell_voxel_grid_tmp.setSaveLeafLayout(true); + map_cell_voxel_grid_tmp.filter(*map_cell_downsampled_pc_ptr_tmp); + + MapGridVoxelInfo current_voxel_grid_list_item; + current_voxel_grid_list_item.min_b_x = map_cell_to_add.metadata.min_x; + current_voxel_grid_list_item.min_b_y = map_cell_to_add.metadata.min_y; + current_voxel_grid_list_item.max_b_x = map_cell_to_add.metadata.max_x; + current_voxel_grid_list_item.max_b_y = map_cell_to_add.metadata.max_y; + + current_voxel_grid_list_item.map_cell_voxel_grid.set_voxel_grid( + &(map_cell_voxel_grid_tmp.leaf_layout_), map_cell_voxel_grid_tmp.get_min_b(), + map_cell_voxel_grid_tmp.get_max_b(), map_cell_voxel_grid_tmp.get_div_b(), + map_cell_voxel_grid_tmp.get_divb_mul(), map_cell_voxel_grid_tmp.get_inverse_leaf_size()); + + current_voxel_grid_list_item.map_cell_pc_ptr.reset(new pcl::PointCloud); + current_voxel_grid_list_item.map_cell_pc_ptr = std::move(map_cell_downsampled_pc_ptr_tmp); + // add + (*mutex_ptr_).lock(); + current_voxel_grid_dict_.insert({map_cell_to_add.cell_id, current_voxel_grid_list_item}); + (*mutex_ptr_).unlock(); + } +}; + +} // namespace autoware::compare_map_segmentation + +#endif // VOXEL_GRID_MAP_LOADER__VOXEL_GRID_MAP_LOADER_HPP_ diff --git a/perception/detected_object_feature_remover/CMakeLists.txt b/perception/detected_object_feature_remover/CMakeLists.txt new file mode 100644 index 0000000000000..e0ff35d75e64a --- /dev/null +++ b/perception/detected_object_feature_remover/CMakeLists.txt @@ -0,0 +1,19 @@ +cmake_minimum_required(VERSION 3.14) +project(detected_object_feature_remover) + +find_package(autoware_cmake REQUIRED) +autoware_package() + +ament_auto_add_library(${PROJECT_NAME} SHARED + src/detected_object_feature_remover_node.cpp +) + +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "autoware::detected_object_feature_remover::DetectedObjectFeatureRemover" + EXECUTABLE detected_object_feature_remover_node +) + +ament_auto_package( + INSTALL_TO_SHARE + launch +) diff --git a/perception/detected_object_validation/include/autoware/detected_object_validation/utils/utils.hpp b/perception/detected_object_validation/include/autoware/detected_object_validation/utils/utils.hpp new file mode 100644 index 0000000000000..3ec119d2a48c5 --- /dev/null +++ b/perception/detected_object_validation/include/autoware/detected_object_validation/utils/utils.hpp @@ -0,0 +1,56 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__DETECTED_OBJECT_VALIDATION__UTILS__UTILS_HPP_ +#define AUTOWARE__DETECTED_OBJECT_VALIDATION__UTILS__UTILS_HPP_ + +#include + +#include + +namespace autoware::detected_object_validation +{ +namespace utils +{ +struct FilterTargetLabel +{ + bool UNKNOWN; + bool CAR; + bool TRUCK; + bool BUS; + bool TRAILER; + bool MOTORCYCLE; + bool BICYCLE; + bool PEDESTRIAN; + bool isTarget(const uint8_t label) const; +}; // struct FilterTargetLabel + +inline bool hasBoundingBox(const autoware_perception_msgs::msg::DetectedObject & object) +{ + if (object.shape.type == autoware_perception_msgs::msg::Shape::BOUNDING_BOX) { + return true; + } else if (object.shape.type == autoware_perception_msgs::msg::Shape::CYLINDER) { + return true; + } else if (object.shape.type == autoware_perception_msgs::msg::Shape::POLYGON) { + return false; + } else { + // unknown shape type. + return false; + } +} + +} // namespace utils +} // namespace autoware::detected_object_validation + +#endif // AUTOWARE__DETECTED_OBJECT_VALIDATION__UTILS__UTILS_HPP_ diff --git a/perception/detected_object_validation/lib/utils/utils.cpp b/perception/detected_object_validation/lib/utils/utils.cpp new file mode 100644 index 0000000000000..327f65a0c833e --- /dev/null +++ b/perception/detected_object_validation/lib/utils/utils.cpp @@ -0,0 +1,34 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "autoware/detected_object_validation/utils/utils.hpp" + +#include + +namespace autoware::detected_object_validation +{ +namespace utils +{ +using Label = autoware_perception_msgs::msg::ObjectClassification; + +bool FilterTargetLabel::isTarget(const uint8_t label) const +{ + return (label == Label::UNKNOWN && UNKNOWN) || (label == Label::CAR && CAR) || + (label == Label::TRUCK && TRUCK) || (label == Label::BUS && BUS) || + (label == Label::TRAILER && TRAILER) || (label == Label::MOTORCYCLE && MOTORCYCLE) || + (label == Label::BICYCLE && BICYCLE) || (label == Label::PEDESTRIAN && PEDESTRIAN); +} + +} // namespace utils +} // namespace autoware::detected_object_validation diff --git a/perception/detected_object_validation/src/lanelet_filter/lanelet_filter.cpp b/perception/detected_object_validation/src/lanelet_filter/lanelet_filter.cpp new file mode 100644 index 0000000000000..6437134bc98a5 --- /dev/null +++ b/perception/detected_object_validation/src/lanelet_filter/lanelet_filter.cpp @@ -0,0 +1,333 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "lanelet_filter.hpp" + +#include "autoware/universe_utils/geometry/geometry.hpp" +#include "autoware_lanelet2_extension/utility/message_conversion.hpp" +#include "autoware_lanelet2_extension/utility/query.hpp" +#include "object_recognition_utils/object_recognition_utils.hpp" + +#include +#include +#include + +#include +#include +#include + +namespace autoware::detected_object_validation +{ +namespace lanelet_filter +{ +ObjectLaneletFilterNode::ObjectLaneletFilterNode(const rclcpp::NodeOptions & node_options) +: Node("object_lanelet_filter_node", node_options), + tf_buffer_(this->get_clock()), + tf_listener_(tf_buffer_) +{ + using std::placeholders::_1; + + // Set parameters + filter_target_.UNKNOWN = declare_parameter("filter_target_label.UNKNOWN", false); + filter_target_.CAR = declare_parameter("filter_target_label.CAR", false); + filter_target_.TRUCK = declare_parameter("filter_target_label.TRUCK", false); + filter_target_.BUS = declare_parameter("filter_target_label.BUS", false); + filter_target_.TRAILER = declare_parameter("filter_target_label.TRAILER", false); + filter_target_.MOTORCYCLE = declare_parameter("filter_target_label.MOTORCYCLE", false); + filter_target_.BICYCLE = declare_parameter("filter_target_label.BICYCLE", false); + filter_target_.PEDESTRIAN = declare_parameter("filter_target_label.PEDESTRIAN", false); + // Set filter settings + filter_settings_.polygon_overlap_filter = + declare_parameter("filter_settings.polygon_overlap_filter.enabled"); + filter_settings_.lanelet_direction_filter = + declare_parameter("filter_settings.lanelet_direction_filter.enabled"); + filter_settings_.lanelet_direction_filter_velocity_yaw_threshold = + declare_parameter("filter_settings.lanelet_direction_filter.velocity_yaw_threshold"); + filter_settings_.lanelet_direction_filter_object_speed_threshold = + declare_parameter("filter_settings.lanelet_direction_filter.object_speed_threshold"); + + // Set publisher/subscriber + map_sub_ = this->create_subscription( + "input/vector_map", rclcpp::QoS{1}.transient_local(), + std::bind(&ObjectLaneletFilterNode::mapCallback, this, _1)); + object_sub_ = this->create_subscription( + "input/object", rclcpp::QoS{1}, std::bind(&ObjectLaneletFilterNode::objectCallback, this, _1)); + object_pub_ = this->create_publisher( + "output/object", rclcpp::QoS{1}); + + debug_publisher_ = + std::make_unique(this, "object_lanelet_filter"); + published_time_publisher_ = + std::make_unique(this); +} + +void ObjectLaneletFilterNode::mapCallback( + const autoware_map_msgs::msg::LaneletMapBin::ConstSharedPtr map_msg) +{ + lanelet_frame_id_ = map_msg->header.frame_id; + lanelet_map_ptr_ = std::make_shared(); + lanelet::utils::conversion::fromBinMsg(*map_msg, lanelet_map_ptr_); +} + +void ObjectLaneletFilterNode::objectCallback( + const autoware_perception_msgs::msg::DetectedObjects::ConstSharedPtr input_msg) +{ + // Guard + if (object_pub_->get_subscription_count() < 1) return; + + autoware_perception_msgs::msg::DetectedObjects output_object_msg; + output_object_msg.header = input_msg->header; + + if (!lanelet_map_ptr_) { + RCLCPP_ERROR(get_logger(), "No vector map received."); + return; + } + autoware_perception_msgs::msg::DetectedObjects transformed_objects; + if (!object_recognition_utils::transformObjects( + *input_msg, lanelet_frame_id_, tf_buffer_, transformed_objects)) { + RCLCPP_ERROR(get_logger(), "Failed transform to %s.", lanelet_frame_id_.c_str()); + return; + } + + // calculate convex hull + const auto convex_hull = getConvexHull(transformed_objects); + + // get intersected lanelets + lanelet::ConstLanelets intersected_lanelets = getIntersectedLanelets(convex_hull); + + // filtering process + for (size_t index = 0; index < transformed_objects.objects.size(); ++index) { + const auto & transformed_object = transformed_objects.objects.at(index); + const auto & input_object = input_msg->objects.at(index); + filterObject(transformed_object, input_object, intersected_lanelets, output_object_msg); + } + object_pub_->publish(output_object_msg); + published_time_publisher_->publish_if_subscribed(object_pub_, output_object_msg.header.stamp); + + // Publish debug info + const double pipeline_latency = + std::chrono::duration( + std::chrono::nanoseconds( + (this->get_clock()->now() - output_object_msg.header.stamp).nanoseconds())) + .count(); + debug_publisher_->publish( + "debug/pipeline_latency_ms", pipeline_latency); +} + +bool ObjectLaneletFilterNode::filterObject( + const autoware_perception_msgs::msg::DetectedObject & transformed_object, + const autoware_perception_msgs::msg::DetectedObject & input_object, + const lanelet::ConstLanelets & intersected_lanelets, + autoware_perception_msgs::msg::DetectedObjects & output_object_msg) +{ + const auto & label = transformed_object.classification.front().label; + if (filter_target_.isTarget(label)) { + bool filter_pass = true; + // 1. is polygon overlap with road lanelets or shoulder lanelets + if (filter_settings_.polygon_overlap_filter) { + const bool is_polygon_overlap = + isObjectOverlapLanelets(transformed_object, intersected_lanelets); + filter_pass = filter_pass && is_polygon_overlap; + } + + // 2. check if objects velocity is the same with the lanelet direction + const bool orientation_not_available = + transformed_object.kinematics.orientation_availability == + autoware_perception_msgs::msg::TrackedObjectKinematics::UNAVAILABLE; + if (filter_settings_.lanelet_direction_filter && !orientation_not_available) { + const bool is_same_direction = + isSameDirectionWithLanelets(intersected_lanelets, transformed_object); + filter_pass = filter_pass && is_same_direction; + } + + // push back to output object + if (filter_pass) { + output_object_msg.objects.emplace_back(input_object); + return true; + } + } else { + output_object_msg.objects.emplace_back(input_object); + return true; + } + return false; +} + +geometry_msgs::msg::Polygon ObjectLaneletFilterNode::setFootprint( + const autoware_perception_msgs::msg::DetectedObject & detected_object) +{ + geometry_msgs::msg::Polygon footprint; + if (detected_object.shape.type == autoware_perception_msgs::msg::Shape::BOUNDING_BOX) { + const auto object_size = detected_object.shape.dimensions; + const double x_front = object_size.x / 2.0; + const double x_rear = -object_size.x / 2.0; + const double y_left = object_size.y / 2.0; + const double y_right = -object_size.y / 2.0; + + footprint.points.push_back( + geometry_msgs::build().x(x_front).y(y_left).z(0.0)); + footprint.points.push_back( + geometry_msgs::build().x(x_front).y(y_right).z(0.0)); + footprint.points.push_back( + geometry_msgs::build().x(x_rear).y(y_right).z(0.0)); + footprint.points.push_back( + geometry_msgs::build().x(x_rear).y(y_left).z(0.0)); + } else { + footprint = detected_object.shape.footprint; + } + return footprint; +} + +LinearRing2d ObjectLaneletFilterNode::getConvexHull( + const autoware_perception_msgs::msg::DetectedObjects & detected_objects) +{ + MultiPoint2d candidate_points; + for (const auto & object : detected_objects.objects) { + const auto & pos = object.kinematics.pose_with_covariance.pose.position; + const auto footprint = setFootprint(object); + for (const auto & p : footprint.points) { + candidate_points.emplace_back(p.x + pos.x, p.y + pos.y); + } + } + + LinearRing2d convex_hull; + boost::geometry::convex_hull(candidate_points, convex_hull); + + return convex_hull; +} + +// fetch the intersected candidate lanelets with bounding box and then +// check the intersections among the lanelets and the convex hull +lanelet::ConstLanelets ObjectLaneletFilterNode::getIntersectedLanelets( + const LinearRing2d & convex_hull) +{ + namespace bg = boost::geometry; + + lanelet::ConstLanelets intersected_lanelets; + + // convert convex_hull to a 2D bounding box for searching in the LaneletMap + bg::model::box> bbox2d_convex_hull; + bg::envelope(convex_hull, bbox2d_convex_hull); + lanelet::BoundingBox2d bbox2d( + lanelet::BasicPoint2d( + bg::get(bbox2d_convex_hull), + bg::get(bbox2d_convex_hull)), + lanelet::BasicPoint2d( + bg::get(bbox2d_convex_hull), + bg::get(bbox2d_convex_hull))); + + lanelet::Lanelets candidates_lanelets = lanelet_map_ptr_->laneletLayer.search(bbox2d); + for (const auto & lanelet : candidates_lanelets) { + // only check the road lanelets and road shoulder lanelets + if ( + lanelet.hasAttribute(lanelet::AttributeName::Subtype) && + (lanelet.attribute(lanelet::AttributeName::Subtype).value() == + lanelet::AttributeValueString::Road || + lanelet.attribute(lanelet::AttributeName::Subtype).value() == "road_shoulder")) { + if (boost::geometry::intersects(convex_hull, lanelet.polygon2d().basicPolygon())) { + intersected_lanelets.emplace_back(lanelet); + } + } + } + + return intersected_lanelets; +} + +bool ObjectLaneletFilterNode::isObjectOverlapLanelets( + const autoware_perception_msgs::msg::DetectedObject & object, + const lanelet::ConstLanelets & intersected_lanelets) +{ + // if has bounding box, use polygon overlap + if (utils::hasBoundingBox(object)) { + Polygon2d polygon; + const auto footprint = setFootprint(object); + for (const auto & point : footprint.points) { + const geometry_msgs::msg::Point32 point_transformed = + autoware::universe_utils::transformPoint( + point, object.kinematics.pose_with_covariance.pose); + polygon.outer().emplace_back(point_transformed.x, point_transformed.y); + } + polygon.outer().push_back(polygon.outer().front()); + return isPolygonOverlapLanelets(polygon, intersected_lanelets); + } else { + // if object do not have bounding box, check each footprint is inside polygon + for (const auto & point : object.shape.footprint.points) { + const geometry_msgs::msg::Point32 point_transformed = + autoware::universe_utils::transformPoint( + point, object.kinematics.pose_with_covariance.pose); + geometry_msgs::msg::Pose point2d; + point2d.position.x = point_transformed.x; + point2d.position.y = point_transformed.y; + for (const auto & lanelet : intersected_lanelets) { + if (lanelet::utils::isInLanelet(point2d, lanelet, 0.0)) { + return true; + } + } + } + return false; + } +} + +bool ObjectLaneletFilterNode::isPolygonOverlapLanelets( + const Polygon2d & polygon, const lanelet::ConstLanelets & intersected_lanelets) +{ + for (const auto & lanelet : intersected_lanelets) { + if (!boost::geometry::disjoint(polygon, lanelet.polygon2d().basicPolygon())) { + return true; + } + } + return false; +} + +bool ObjectLaneletFilterNode::isSameDirectionWithLanelets( + const lanelet::ConstLanelets & lanelets, + const autoware_perception_msgs::msg::DetectedObject & object) +{ + const double object_yaw = tf2::getYaw(object.kinematics.pose_with_covariance.pose.orientation); + const double object_velocity_norm = std::hypot( + object.kinematics.twist_with_covariance.twist.linear.x, + object.kinematics.twist_with_covariance.twist.linear.y); + const double object_velocity_yaw = std::atan2( + object.kinematics.twist_with_covariance.twist.linear.y, + object.kinematics.twist_with_covariance.twist.linear.x) + + object_yaw; + + if (object_velocity_norm < filter_settings_.lanelet_direction_filter_object_speed_threshold) { + return true; + } + for (const auto & lanelet : lanelets) { + const bool is_in_lanelet = + lanelet::utils::isInLanelet(object.kinematics.pose_with_covariance.pose, lanelet, 0.0); + if (!is_in_lanelet) { + continue; + } + const double lane_yaw = lanelet::utils::getLaneletAngle( + lanelet, object.kinematics.pose_with_covariance.pose.position); + const double delta_yaw = object_velocity_yaw - lane_yaw; + const double normalized_delta_yaw = autoware::universe_utils::normalizeRadian(delta_yaw); + const double abs_norm_delta_yaw = std::fabs(normalized_delta_yaw); + + if (abs_norm_delta_yaw < filter_settings_.lanelet_direction_filter_velocity_yaw_threshold) { + return true; + } + } + + return false; +} + +} // namespace lanelet_filter +} // namespace autoware::detected_object_validation + +#include +RCLCPP_COMPONENTS_REGISTER_NODE( + autoware::detected_object_validation::lanelet_filter::ObjectLaneletFilterNode) diff --git a/perception/detected_object_validation/src/lanelet_filter/lanelet_filter.hpp b/perception/detected_object_validation/src/lanelet_filter/lanelet_filter.hpp new file mode 100644 index 0000000000000..3f7a53d0319be --- /dev/null +++ b/perception/detected_object_validation/src/lanelet_filter/lanelet_filter.hpp @@ -0,0 +1,96 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef LANELET_FILTER__LANELET_FILTER_HPP_ +#define LANELET_FILTER__LANELET_FILTER_HPP_ + +#include "autoware/detected_object_validation/utils/utils.hpp" +#include "autoware/universe_utils/geometry/geometry.hpp" +#include "autoware/universe_utils/ros/debug_publisher.hpp" +#include "autoware/universe_utils/ros/published_time_publisher.hpp" +#include "autoware_lanelet2_extension/utility/utilities.hpp" + +#include + +#include "autoware_map_msgs/msg/lanelet_map_bin.hpp" +#include "autoware_perception_msgs/msg/detected_objects.hpp" + +#include +#include +#include + +#include +#include + +namespace autoware::detected_object_validation +{ +namespace lanelet_filter +{ +using autoware::universe_utils::LinearRing2d; +using autoware::universe_utils::MultiPoint2d; +using autoware::universe_utils::Point2d; +using autoware::universe_utils::Polygon2d; + +class ObjectLaneletFilterNode : public rclcpp::Node +{ +public: + explicit ObjectLaneletFilterNode(const rclcpp::NodeOptions & node_options); + +private: + void objectCallback(const autoware_perception_msgs::msg::DetectedObjects::ConstSharedPtr); + void mapCallback(const autoware_map_msgs::msg::LaneletMapBin::ConstSharedPtr); + + rclcpp::Publisher::SharedPtr object_pub_; + rclcpp::Subscription::SharedPtr map_sub_; + rclcpp::Subscription::SharedPtr object_sub_; + std::unique_ptr debug_publisher_{nullptr}; + + lanelet::LaneletMapPtr lanelet_map_ptr_; + std::string lanelet_frame_id_; + + tf2_ros::Buffer tf_buffer_; + tf2_ros::TransformListener tf_listener_; + + utils::FilterTargetLabel filter_target_; + struct FilterSettings + { + bool polygon_overlap_filter; + bool lanelet_direction_filter; + double lanelet_direction_filter_velocity_yaw_threshold; + double lanelet_direction_filter_object_speed_threshold; + } filter_settings_; + + bool filterObject( + const autoware_perception_msgs::msg::DetectedObject & transformed_object, + const autoware_perception_msgs::msg::DetectedObject & input_object, + const lanelet::ConstLanelets & intersected_lanelets, + autoware_perception_msgs::msg::DetectedObjects & output_object_msg); + LinearRing2d getConvexHull(const autoware_perception_msgs::msg::DetectedObjects &); + lanelet::ConstLanelets getIntersectedLanelets(const LinearRing2d &); + bool isObjectOverlapLanelets( + const autoware_perception_msgs::msg::DetectedObject & object, + const lanelet::ConstLanelets & intersected_lanelets); + bool isPolygonOverlapLanelets(const Polygon2d &, const lanelet::ConstLanelets &); + bool isSameDirectionWithLanelets( + const lanelet::ConstLanelets & lanelets, + const autoware_perception_msgs::msg::DetectedObject & object); + geometry_msgs::msg::Polygon setFootprint(const autoware_perception_msgs::msg::DetectedObject &); + + std::unique_ptr published_time_publisher_; +}; + +} // namespace lanelet_filter +} // namespace autoware::detected_object_validation + +#endif // LANELET_FILTER__LANELET_FILTER_HPP_ diff --git a/perception/detected_object_validation/src/obstacle_pointcloud/debugger.hpp b/perception/detected_object_validation/src/obstacle_pointcloud/debugger.hpp new file mode 100644 index 0000000000000..5886474d51b34 --- /dev/null +++ b/perception/detected_object_validation/src/obstacle_pointcloud/debugger.hpp @@ -0,0 +1,115 @@ +// Copyright 2021 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef OBSTACLE_POINTCLOUD__DEBUGGER_HPP_ +#define OBSTACLE_POINTCLOUD__DEBUGGER_HPP_ + +#include + +#include "autoware_perception_msgs/msg/detected_objects.hpp" +#include + +#include +#include +#include + +namespace autoware::detected_object_validation +{ +namespace obstacle_pointcloud +{ +class Debugger +{ +public: + explicit Debugger(rclcpp::Node * node) + : neighbor_pointcloud_(new pcl::PointCloud), + pointcloud_within_polygon_(new pcl::PointCloud) + { + removed_objects_pub_ = node->create_publisher( + "~/debug/removed_objects", 1); + neighbor_pointcloud_pub_ = + node->create_publisher("~/debug/neighbor_pointcloud", 1); + pointcloud_within_polygon_pub_ = + node->create_publisher("~/debug/pointcloud_within_polygon", 1); + } + + ~Debugger() {} + void publishRemovedObjects(const autoware_perception_msgs::msg::DetectedObjects & input) + { + removed_objects_pub_->publish(input); + } + void publishNeighborPointcloud(const std_msgs::msg::Header & header) + { + sensor_msgs::msg::PointCloud2 output; + pcl::toROSMsg(*neighbor_pointcloud_, output); + output.header = header; + neighbor_pointcloud_pub_->publish(output); + neighbor_pointcloud_->clear(); + } + + void publishPointcloudWithinPolygon(const std_msgs::msg::Header & header) + { + sensor_msgs::msg::PointCloud2 output; + pcl::toROSMsg(*pointcloud_within_polygon_, output); + output.header = header; + pointcloud_within_polygon_pub_->publish(output); + pointcloud_within_polygon_->clear(); + } + + void addNeighborPointcloud(const pcl::PointCloud::Ptr & input) + { + pcl::PointCloud::Ptr input_xyz = toXYZ(input); + addNeighborPointcloud(input_xyz); + } + + void addNeighborPointcloud(const pcl::PointCloud::Ptr & input) + { + if (!input->empty()) { + neighbor_pointcloud_->reserve(neighbor_pointcloud_->size() + input->size()); + for (const auto & point : *input) { + neighbor_pointcloud_->push_back(point); + } + } + } + void addPointcloudWithinPolygon(const pcl::PointCloud::Ptr & input) + { + // pcl::PointCloud::Ptr input_xyz = toXYZ(input); + for (const auto & point : *input) { + pointcloud_within_polygon_->push_back(point); + } + } + +private: + rclcpp::Publisher::SharedPtr removed_objects_pub_; + rclcpp::Publisher::SharedPtr neighbor_pointcloud_pub_; + rclcpp::Publisher::SharedPtr pointcloud_within_polygon_pub_; + pcl::PointCloud::Ptr neighbor_pointcloud_; + pcl::PointCloud::Ptr pointcloud_within_polygon_; + +private: + inline pcl::PointCloud::Ptr toXYZ( + const pcl::PointCloud::Ptr & pointcloud) + { + pcl::PointCloud::Ptr pointcloud_xyz(new pcl::PointCloud); + pointcloud_xyz->reserve(pointcloud->size()); + for (const auto & point : *pointcloud) { + pointcloud_xyz->push_back(pcl::PointXYZ(point.x, point.y, 0.0)); + } + return pointcloud_xyz; + } +}; + +} // namespace obstacle_pointcloud +} // namespace autoware::detected_object_validation + +#endif // OBSTACLE_POINTCLOUD__DEBUGGER_HPP_ diff --git a/perception/detected_object_validation/src/obstacle_pointcloud/obstacle_pointcloud_validator.cpp b/perception/detected_object_validation/src/obstacle_pointcloud/obstacle_pointcloud_validator.cpp new file mode 100644 index 0000000000000..c5ecce714735b --- /dev/null +++ b/perception/detected_object_validation/src/obstacle_pointcloud/obstacle_pointcloud_validator.cpp @@ -0,0 +1,380 @@ +// Copyright 2022 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#define EIGEN_MPL2_ONLY + +#include "obstacle_pointcloud_validator.hpp" + +#include +#include + +#include + +#ifdef ROS_DISTRO_GALACTIC +#include +#else +#include +#endif + +#include +#include + +namespace autoware::detected_object_validation +{ +namespace obstacle_pointcloud +{ +namespace bg = boost::geometry; +using Shape = autoware_perception_msgs::msg::Shape; +using Polygon2d = autoware::universe_utils::Polygon2d; + +Validator::Validator(const PointsNumThresholdParam & points_num_threshold_param) +{ + points_num_threshold_param_.min_points_num = points_num_threshold_param.min_points_num; + points_num_threshold_param_.max_points_num = points_num_threshold_param.max_points_num; + points_num_threshold_param_.min_points_and_distance_ratio = + points_num_threshold_param.min_points_and_distance_ratio; +} + +size_t Validator::getThresholdPointCloud( + const autoware_perception_msgs::msg::DetectedObject & object) +{ + const auto object_label_id = object.classification.front().label; + const auto object_distance = std::hypot( + object.kinematics.pose_with_covariance.pose.position.x, + object.kinematics.pose_with_covariance.pose.position.y); + size_t threshold_pc = std::clamp( + static_cast( + points_num_threshold_param_.min_points_and_distance_ratio.at(object_label_id) / + object_distance + + 0.5f), + static_cast(points_num_threshold_param_.min_points_num.at(object_label_id)), + static_cast(points_num_threshold_param_.max_points_num.at(object_label_id))); + return threshold_pc; +} + +Validator2D::Validator2D(PointsNumThresholdParam & points_num_threshold_param) +: Validator(points_num_threshold_param) +{ +} + +bool Validator2D::setKdtreeInputCloud( + const sensor_msgs::msg::PointCloud2::ConstSharedPtr & input_cloud) +{ + obstacle_pointcloud_.reset(new pcl::PointCloud); + pcl::fromROSMsg(*input_cloud, *obstacle_pointcloud_); + if (obstacle_pointcloud_->empty()) { + return false; + } + + kdtree_ = pcl::make_shared>(false); + kdtree_->setInputCloud(obstacle_pointcloud_); + + return true; +} +pcl::PointCloud::Ptr Validator2D::convertToXYZ( + const pcl::PointCloud::Ptr & pointcloud_xy) +{ + pcl::PointCloud::Ptr pointcloud_xyz(new pcl::PointCloud); + pointcloud_xyz->reserve(pointcloud_xy->size()); + for (const auto & point : *pointcloud_xy) { + pointcloud_xyz->push_back(pcl::PointXYZ(point.x, point.y, 0.0)); + } + return pointcloud_xyz; +} + +std::optional Validator2D::getPointCloudWithinObject( + const autoware_perception_msgs::msg::DetectedObject & object, + const pcl::PointCloud::Ptr pointcloud) +{ + std::vector vertices_array; + pcl::Vertices vertices; + Polygon2d poly2d = autoware::universe_utils::toPolygon2d( + object.kinematics.pose_with_covariance.pose, object.shape); + if (bg::is_empty(poly2d)) return std::nullopt; + + pcl::PointCloud::Ptr poly3d(new pcl::PointCloud); + + for (size_t i = 0; i < poly2d.outer().size(); ++i) { + vertices.vertices.emplace_back(i); + vertices_array.emplace_back(vertices); + poly3d->emplace_back(poly2d.outer().at(i).x(), poly2d.outer().at(i).y(), 0.0); + } + cropped_pointcloud_.reset(new pcl::PointCloud); + pcl::CropHull cropper; // don't be implemented PointXY by PCL + cropper.setInputCloud(convertToXYZ(pointcloud)); + cropper.setDim(2); + cropper.setHullIndices(vertices_array); + cropper.setHullCloud(poly3d); + cropper.setCropOutside(true); + cropper.filter(*cropped_pointcloud_); + return cropped_pointcloud_->size(); +} + +bool Validator2D::validate_object( + const autoware_perception_msgs::msg::DetectedObject & transformed_object) +{ + // get neighbor_pointcloud of object + neighbor_pointcloud_.reset(new pcl::PointCloud); + std::vector indices; + std::vector distances; + const auto search_radius = getMaxRadius(transformed_object); + if (!search_radius) { + return false; + } + kdtree_->radiusSearch( + pcl::PointXY( + transformed_object.kinematics.pose_with_covariance.pose.position.x, + transformed_object.kinematics.pose_with_covariance.pose.position.y), + search_radius.value(), indices, distances); + for (const auto & index : indices) { + neighbor_pointcloud_->push_back(obstacle_pointcloud_->at(index)); + } + const auto num = getPointCloudWithinObject(transformed_object, neighbor_pointcloud_); + if (!num) return true; + + size_t threshold_pointcloud_num = getThresholdPointCloud(transformed_object); + if (num.value() > threshold_pointcloud_num) { + return true; + } + return false; // remove object +} + +std::optional Validator2D::getMaxRadius( + const autoware_perception_msgs::msg::DetectedObject & object) +{ + if (object.shape.type == Shape::BOUNDING_BOX || object.shape.type == Shape::CYLINDER) { + return std::hypot(object.shape.dimensions.x * 0.5f, object.shape.dimensions.y * 0.5f); + } else if (object.shape.type == Shape::POLYGON) { + float max_dist = 0.0; + for (const auto & point : object.shape.footprint.points) { + const float dist = std::hypot(point.x, point.y); + max_dist = max_dist < dist ? dist : max_dist; + } + return max_dist; + } else { + return std::nullopt; + } +} + +Validator3D::Validator3D(PointsNumThresholdParam & points_num_threshold_param) +: Validator(points_num_threshold_param) +{ +} +bool Validator3D::setKdtreeInputCloud( + const sensor_msgs::msg::PointCloud2::ConstSharedPtr & input_cloud) +{ + obstacle_pointcloud_.reset(new pcl::PointCloud); + pcl::fromROSMsg(*input_cloud, *obstacle_pointcloud_); + if (obstacle_pointcloud_->empty()) { + return false; + } + // setup kdtree_ + kdtree_ = pcl::make_shared>(false); + kdtree_->setInputCloud(obstacle_pointcloud_); + return true; +} +std::optional Validator3D::getMaxRadius( + const autoware_perception_msgs::msg::DetectedObject & object) +{ + if (object.shape.type == Shape::BOUNDING_BOX || object.shape.type == Shape::CYLINDER) { + auto square_radius = (object.shape.dimensions.x * 0.5f) * (object.shape.dimensions.x * 0.5f) + + (object.shape.dimensions.y * 0.5f) * (object.shape.dimensions.y * 0.5f) + + (object.shape.dimensions.z * 0.5f) * (object.shape.dimensions.z * 0.5f); + return std::sqrt(square_radius); + } else if (object.shape.type == Shape::POLYGON) { + float max_dist = 0.0; + for (const auto & point : object.shape.footprint.points) { + const float dist = std::hypot(point.x, point.y); + max_dist = max_dist < dist ? dist : max_dist; + } + return std::hypot(max_dist, object.shape.dimensions.z * 0.5f); + } else { + return std::nullopt; + } +} + +std::optional Validator3D::getPointCloudWithinObject( + const autoware_perception_msgs::msg::DetectedObject & object, + const pcl::PointCloud::Ptr neighbor_pointcloud) +{ + std::vector vertices_array; + pcl::Vertices vertices; + auto const & object_position = object.kinematics.pose_with_covariance.pose.position; + auto const object_height = object.shape.dimensions.x; + auto z_min = object_position.z - object_height / 2.0f; + auto z_max = object_position.z + object_height / 2.0f; + Polygon2d poly2d = autoware::universe_utils::toPolygon2d( + object.kinematics.pose_with_covariance.pose, object.shape); + if (bg::is_empty(poly2d)) return std::nullopt; + + pcl::PointCloud::Ptr poly3d(new pcl::PointCloud); + for (size_t i = 0; i < poly2d.outer().size(); ++i) { + vertices.vertices.emplace_back(i); + vertices_array.emplace_back(vertices); + poly3d->emplace_back(poly2d.outer().at(i).x(), poly2d.outer().at(i).y(), 0.0); + } + + pcl::PointCloud::Ptr cropped_pointcloud_2d(new pcl::PointCloud); + pcl::CropHull cropper; + cropper.setInputCloud(neighbor_pointcloud); + cropper.setDim(2); + cropper.setHullIndices(vertices_array); + cropper.setHullCloud(poly3d); + cropper.setCropOutside(true); + cropper.filter(*cropped_pointcloud_2d); + + cropped_pointcloud_.reset(new pcl::PointCloud); + cropped_pointcloud_->reserve(cropped_pointcloud_2d->size()); + for (const auto & point : *cropped_pointcloud_2d) { + if (point.z > z_min && point.z < z_max) { + cropped_pointcloud_->push_back(point); + } + } + return cropped_pointcloud_->size(); +} + +bool Validator3D::validate_object( + const autoware_perception_msgs::msg::DetectedObject & transformed_object) +{ + neighbor_pointcloud_.reset(new pcl::PointCloud); + std::vector indices; + std::vector distances; + const auto search_radius = getMaxRadius(transformed_object); + if (!search_radius) { + return false; + } + kdtree_->radiusSearch( + pcl::PointXYZ( + transformed_object.kinematics.pose_with_covariance.pose.position.x, + transformed_object.kinematics.pose_with_covariance.pose.position.y, + transformed_object.kinematics.pose_with_covariance.pose.position.z), + search_radius.value(), indices, distances); + for (const auto & index : indices) { + neighbor_pointcloud_->push_back(obstacle_pointcloud_->at(index)); + } + + const auto num = getPointCloudWithinObject(transformed_object, neighbor_pointcloud_); + if (!num) return true; + + size_t threshold_pointcloud_num = getThresholdPointCloud(transformed_object); + if (num.value() > threshold_pointcloud_num) { + return true; + } + return false; // remove object +} + +ObstaclePointCloudBasedValidator::ObstaclePointCloudBasedValidator( + const rclcpp::NodeOptions & node_options) +: rclcpp::Node("obstacle_pointcloud_based_validator", node_options), + objects_sub_(this, "~/input/detected_objects", rclcpp::QoS{1}.get_rmw_qos_profile()), + obstacle_pointcloud_sub_( + this, "~/input/obstacle_pointcloud", + rclcpp::SensorDataQoS{}.keep_last(1).get_rmw_qos_profile()), + tf_buffer_(get_clock()), + tf_listener_(tf_buffer_), + sync_(SyncPolicy(10), objects_sub_, obstacle_pointcloud_sub_) +{ + points_num_threshold_param_.min_points_num = + declare_parameter>("min_points_num"); + points_num_threshold_param_.max_points_num = + declare_parameter>("max_points_num"); + points_num_threshold_param_.min_points_and_distance_ratio = + declare_parameter>("min_points_and_distance_ratio"); + + using_2d_validator_ = declare_parameter("using_2d_validator"); + + using std::placeholders::_1; + using std::placeholders::_2; + + sync_.registerCallback( + std::bind(&ObstaclePointCloudBasedValidator::onObjectsAndObstaclePointCloud, this, _1, _2)); + if (using_2d_validator_) { + validator_ = std::make_unique(points_num_threshold_param_); + } else { + validator_ = std::make_unique(points_num_threshold_param_); + } + + objects_pub_ = create_publisher( + "~/output/objects", rclcpp::QoS{1}); + debug_publisher_ = std::make_unique( + this, "obstacle_pointcloud_based_validator"); + + const bool enable_debugger = declare_parameter("enable_debugger", false); + if (enable_debugger) debugger_ = std::make_shared(this); + published_time_publisher_ = + std::make_unique(this); +} +void ObstaclePointCloudBasedValidator::onObjectsAndObstaclePointCloud( + const autoware_perception_msgs::msg::DetectedObjects::ConstSharedPtr & input_objects, + const sensor_msgs::msg::PointCloud2::ConstSharedPtr & input_obstacle_pointcloud) +{ + autoware_perception_msgs::msg::DetectedObjects output, removed_objects; + output.header = input_objects->header; + removed_objects.header = input_objects->header; + + // Transform to pointcloud frame + autoware_perception_msgs::msg::DetectedObjects transformed_objects; + if (!object_recognition_utils::transformObjects( + *input_objects, input_obstacle_pointcloud->header.frame_id, tf_buffer_, + transformed_objects)) { + // objects_pub_->publish(*input_objects); + return; + } + bool validation_is_ready = true; + if (!validator_->setKdtreeInputCloud(input_obstacle_pointcloud)) { + RCLCPP_WARN_THROTTLE( + this->get_logger(), *this->get_clock(), 5, + "obstacle pointcloud is empty! Can not validate objects."); + validation_is_ready = false; + } + + for (size_t i = 0; i < transformed_objects.objects.size(); ++i) { + const auto & transformed_object = transformed_objects.objects.at(i); + const auto & object = input_objects->objects.at(i); + const auto validated = + validation_is_ready ? validator_->validate_object(transformed_object) : false; + if (debugger_) { + debugger_->addNeighborPointcloud(validator_->getDebugNeighborPointCloud()); + debugger_->addPointcloudWithinPolygon(validator_->getDebugPointCloudWithinObject()); + } + if (validated) { + output.objects.push_back(object); + } else { + removed_objects.objects.push_back(object); + } + } + + objects_pub_->publish(output); + published_time_publisher_->publish_if_subscribed(objects_pub_, output.header.stamp); + if (debugger_) { + debugger_->publishRemovedObjects(removed_objects); + debugger_->publishNeighborPointcloud(input_obstacle_pointcloud->header); + debugger_->publishPointcloudWithinPolygon(input_obstacle_pointcloud->header); + } + + // Publish processing time info + const double pipeline_latency = + std::chrono::duration( + std::chrono::nanoseconds((this->get_clock()->now() - output.header.stamp).nanoseconds())) + .count(); + debug_publisher_->publish( + "debug/pipeline_latency_ms", pipeline_latency); +} + +} // namespace obstacle_pointcloud +} // namespace autoware::detected_object_validation + +#include +RCLCPP_COMPONENTS_REGISTER_NODE( + autoware::detected_object_validation::obstacle_pointcloud::ObstaclePointCloudBasedValidator) diff --git a/perception/detected_object_validation/src/obstacle_pointcloud/obstacle_pointcloud_validator.hpp b/perception/detected_object_validation/src/obstacle_pointcloud/obstacle_pointcloud_validator.hpp new file mode 100644 index 0000000000000..801a693905f93 --- /dev/null +++ b/perception/detected_object_validation/src/obstacle_pointcloud/obstacle_pointcloud_validator.hpp @@ -0,0 +1,171 @@ +// Copyright 2022 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +// NOLINTNEXTLINE(whitespace/line_length) +#ifndef OBSTACLE_POINTCLOUD__OBSTACLE_POINTCLOUD_VALIDATOR_HPP_ +// NOLINTNEXTLINE(whitespace/line_length) +#define OBSTACLE_POINTCLOUD__OBSTACLE_POINTCLOUD_VALIDATOR_HPP_ + +#include "autoware/universe_utils/ros/debug_publisher.hpp" +#include "autoware/universe_utils/ros/published_time_publisher.hpp" +#include "debugger.hpp" + +#include + +#include "autoware_perception_msgs/msg/detected_objects.hpp" +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include + +namespace autoware::detected_object_validation +{ +namespace obstacle_pointcloud +{ + +struct PointsNumThresholdParam +{ + std::vector min_points_num; + std::vector max_points_num; + std::vector min_points_and_distance_ratio; +}; + +class Validator +{ +private: + PointsNumThresholdParam points_num_threshold_param_; + +protected: + pcl::PointCloud::Ptr cropped_pointcloud_; + +public: + explicit Validator(const PointsNumThresholdParam & points_num_threshold_param); + inline pcl::PointCloud::Ptr getDebugPointCloudWithinObject() + { + return cropped_pointcloud_; + } + + virtual bool setKdtreeInputCloud( + const sensor_msgs::msg::PointCloud2::ConstSharedPtr & input_pointcloud) = 0; + virtual bool validate_object( + const autoware_perception_msgs::msg::DetectedObject & transformed_object) = 0; + virtual std::optional getMaxRadius( + const autoware_perception_msgs::msg::DetectedObject & object) = 0; + size_t getThresholdPointCloud(const autoware_perception_msgs::msg::DetectedObject & object); + virtual pcl::PointCloud::Ptr getDebugNeighborPointCloud() = 0; + + virtual ~Validator() = default; +}; + +class Validator2D : public Validator +{ +private: + pcl::PointCloud::Ptr obstacle_pointcloud_; + pcl::PointCloud::Ptr neighbor_pointcloud_; + pcl::search::Search::Ptr kdtree_; + +public: + explicit Validator2D(PointsNumThresholdParam & points_num_threshold_param); + + pcl::PointCloud::Ptr convertToXYZ( + const pcl::PointCloud::Ptr & pointcloud_xy); + inline pcl::PointCloud::Ptr getDebugNeighborPointCloud() override + { + return convertToXYZ(neighbor_pointcloud_); + } + + bool setKdtreeInputCloud( + const sensor_msgs::msg::PointCloud2::ConstSharedPtr & input_cloud) override; + bool validate_object( + const autoware_perception_msgs::msg::DetectedObject & transformed_object) override; + std::optional getMaxRadius( + const autoware_perception_msgs::msg::DetectedObject & object) override; + std::optional getPointCloudWithinObject( + const autoware_perception_msgs::msg::DetectedObject & object, + const pcl::PointCloud::Ptr pointcloud); +}; +class Validator3D : public Validator +{ +private: + pcl::PointCloud::Ptr obstacle_pointcloud_; + pcl::PointCloud::Ptr neighbor_pointcloud_; + pcl::search::Search::Ptr kdtree_; + +public: + explicit Validator3D(PointsNumThresholdParam & points_num_threshold_param); + inline pcl::PointCloud::Ptr getDebugNeighborPointCloud() override + { + return neighbor_pointcloud_; + } + bool setKdtreeInputCloud( + const sensor_msgs::msg::PointCloud2::ConstSharedPtr & input_cloud) override; + bool validate_object( + const autoware_perception_msgs::msg::DetectedObject & transformed_object) override; + std::optional getMaxRadius( + const autoware_perception_msgs::msg::DetectedObject & object) override; + std::optional getPointCloudWithinObject( + const autoware_perception_msgs::msg::DetectedObject & object, + const pcl::PointCloud::Ptr neighbor_pointcloud); +}; + +class ObstaclePointCloudBasedValidator : public rclcpp::Node +{ +public: + explicit ObstaclePointCloudBasedValidator(const rclcpp::NodeOptions & node_options); + +private: + rclcpp::Publisher::SharedPtr objects_pub_; + message_filters::Subscriber objects_sub_; + message_filters::Subscriber obstacle_pointcloud_sub_; + std::unique_ptr debug_publisher_{nullptr}; + tf2_ros::Buffer tf_buffer_; + tf2_ros::TransformListener tf_listener_; + + typedef message_filters::sync_policies::ApproximateTime< + autoware_perception_msgs::msg::DetectedObjects, sensor_msgs::msg::PointCloud2> + SyncPolicy; + typedef message_filters::Synchronizer Sync; + Sync sync_; + PointsNumThresholdParam points_num_threshold_param_; + + std::shared_ptr debugger_; + bool using_2d_validator_; + std::unique_ptr validator_; + std::unique_ptr published_time_publisher_; + +private: + void onObjectsAndObstaclePointCloud( + const autoware_perception_msgs::msg::DetectedObjects::ConstSharedPtr & input_objects, + const sensor_msgs::msg::PointCloud2::ConstSharedPtr & input_obstacle_pointcloud); +}; + +} // namespace obstacle_pointcloud +} // namespace autoware::detected_object_validation + +// NOLINTNEXTLINE(whitespace/line_length) +#endif // OBSTACLE_POINTCLOUD__OBSTACLE_POINTCLOUD_VALIDATOR_HPP_ diff --git a/perception/detected_object_validation/src/occupancy_grid_map/occupancy_grid_map_validator.cpp b/perception/detected_object_validation/src/occupancy_grid_map/occupancy_grid_map_validator.cpp new file mode 100644 index 0000000000000..c082b4b0f03f1 --- /dev/null +++ b/perception/detected_object_validation/src/occupancy_grid_map/occupancy_grid_map_validator.cpp @@ -0,0 +1,184 @@ +// Copyright 2022 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#define EIGEN_MPL2_ONLY + +#include "occupancy_grid_map_validator.hpp" + +#include "autoware/universe_utils/geometry/boost_polygon_utils.hpp" +#include "object_recognition_utils/object_classification.hpp" +#include "object_recognition_utils/object_recognition_utils.hpp" + +#include + +#ifdef ROS_DISTRO_GALACTIC +#include +#else +#include +#endif + +#include +#include + +namespace autoware::detected_object_validation +{ +namespace occupancy_grid_map +{ +using Shape = autoware_perception_msgs::msg::Shape; +using Polygon2d = autoware::universe_utils::Polygon2d; + +OccupancyGridBasedValidator::OccupancyGridBasedValidator(const rclcpp::NodeOptions & node_options) +: rclcpp::Node("occupancy_grid_based_validator", node_options), + objects_sub_(this, "~/input/detected_objects", rclcpp::QoS{1}.get_rmw_qos_profile()), + occ_grid_sub_(this, "~/input/occupancy_grid_map", rclcpp::QoS{1}.get_rmw_qos_profile()), + tf_buffer_(get_clock()), + tf_listener_(tf_buffer_), + sync_(SyncPolicy(10), objects_sub_, occ_grid_sub_) +{ + using std::placeholders::_1; + using std::placeholders::_2; + sync_.registerCallback( + std::bind(&OccupancyGridBasedValidator::onObjectsAndOccGrid, this, _1, _2)); + objects_pub_ = create_publisher( + "~/output/objects", rclcpp::QoS{1}); + + mean_threshold_ = declare_parameter("mean_threshold", 0.6); + enable_debug_ = declare_parameter("enable_debug", false); + published_time_publisher_ = + std::make_unique(this); +} + +void OccupancyGridBasedValidator::onObjectsAndOccGrid( + const autoware_perception_msgs::msg::DetectedObjects::ConstSharedPtr & input_objects, + const nav_msgs::msg::OccupancyGrid::ConstSharedPtr & input_occ_grid) +{ + autoware_perception_msgs::msg::DetectedObjects output; + output.header = input_objects->header; + + // Transform to occ grid frame + autoware_perception_msgs::msg::DetectedObjects transformed_objects; + if (!object_recognition_utils::transformObjects( + *input_objects, input_occ_grid->header.frame_id, tf_buffer_, transformed_objects)) + return; + + // Convert ros data type to cv::Mat + cv::Mat occ_grid = fromOccupancyGrid(*input_occ_grid); + + // Get vehicle mask image and calculate mean within mask. + for (size_t i = 0; i < transformed_objects.objects.size(); ++i) { + const auto & transformed_object = transformed_objects.objects.at(i); + const auto & object = input_objects->objects.at(i); + const auto & label = object.classification.front().label; + if (object_recognition_utils::isCarLikeVehicle(label)) { + auto mask = getMask(*input_occ_grid, transformed_object); + const float mean = mask ? cv::mean(occ_grid, mask.value())[0] * 0.01 : 1.0; + if (mean_threshold_ < mean) output.objects.push_back(object); + } else { + output.objects.push_back(object); + } + } + + objects_pub_->publish(output); + published_time_publisher_->publish_if_subscribed(objects_pub_, output.header.stamp); + + if (enable_debug_) showDebugImage(*input_occ_grid, transformed_objects, occ_grid); +} + +std::optional OccupancyGridBasedValidator::getMask( + const nav_msgs::msg::OccupancyGrid & occupancy_grid, + const autoware_perception_msgs::msg::DetectedObject & object) +{ + cv::Mat mask = cv::Mat::zeros(occupancy_grid.info.height, occupancy_grid.info.width, CV_8UC1); + return getMask(occupancy_grid, object, mask); +} + +std::optional OccupancyGridBasedValidator::getMask( + const nav_msgs::msg::OccupancyGrid & occupancy_grid, + const autoware_perception_msgs::msg::DetectedObject & object, cv::Mat mask) +{ + const auto & resolution = occupancy_grid.info.resolution; + const auto & origin = occupancy_grid.info.origin; + std::vector pixel_vertices; + Polygon2d poly2d = autoware::universe_utils::toPolygon2d( + object.kinematics.pose_with_covariance.pose, object.shape); + + bool is_polygon_within_image = true; + for (const auto & p : poly2d.outer()) { + const float px = (p.x() - origin.position.x) / resolution; + const float py = (p.y() - origin.position.y) / resolution; + const bool is_point_within_image = (0 <= px && px < mask.cols && 0 <= py && py < mask.rows); + + if (!is_point_within_image) is_polygon_within_image = false; + + pixel_vertices.push_back(cv::Point2f(px, py)); + } + + if (is_polygon_within_image && !pixel_vertices.empty()) { + cv::fillConvexPoly(mask, pixel_vertices, cv::Scalar(255)); + return mask; + } else { + return std::nullopt; + } +} + +cv::Mat OccupancyGridBasedValidator::fromOccupancyGrid( + const nav_msgs::msg::OccupancyGrid & occupancy_grid) +{ + cv::Mat cv_occ_grid = + cv::Mat::zeros(occupancy_grid.info.height, occupancy_grid.info.width, CV_8UC1); + for (size_t i = 0; i < occupancy_grid.data.size(); ++i) { + size_t y = i / occupancy_grid.info.width; + size_t x = i % occupancy_grid.info.width; + const auto & data = occupancy_grid.data[i]; + cv_occ_grid.at(y, x) = + std::min(std::max(data, static_cast(0)), static_cast(50)) * 2; + } + return cv_occ_grid; +} + +void OccupancyGridBasedValidator::showDebugImage( + const nav_msgs::msg::OccupancyGrid & ros_occ_grid, + const autoware_perception_msgs::msg::DetectedObjects & objects, const cv::Mat & occ_grid) +{ + cv::namedWindow("removed_objects_image", cv::WINDOW_NORMAL); + cv::namedWindow("passed_objects_image", cv::WINDOW_NORMAL); + cv::Mat removed_objects_image = occ_grid.clone(); + cv::Mat passed_objects_image = occ_grid.clone(); + + // Get vehicle mask image and calculate mean within mask. + for (const auto & object : objects.objects) { + const auto & label = object.classification.front().label; + if (object_recognition_utils::isCarLikeVehicle(label)) { + auto mask = getMask(ros_occ_grid, object); + const float mean = mask ? cv::mean(occ_grid, mask.value())[0] * 0.01 : 1.0; + if (mean_threshold_ < mean) { + auto passed_mask = getMask(ros_occ_grid, object, passed_objects_image); + if (passed_mask) passed_objects_image = passed_mask.value(); + } else { + auto removed_mask = getMask(ros_occ_grid, object, removed_objects_image); + if (removed_mask) removed_objects_image = removed_mask.value(); + } + } + } + cv::imshow("removed_objects_image", removed_objects_image); + cv::imshow("passed_objects_image", passed_objects_image); + cv::waitKey(2); +} + +} // namespace occupancy_grid_map +} // namespace autoware::detected_object_validation + +#include +RCLCPP_COMPONENTS_REGISTER_NODE( + autoware::detected_object_validation::occupancy_grid_map::OccupancyGridBasedValidator) diff --git a/perception/detected_object_validation/src/occupancy_grid_map/occupancy_grid_map_validator.hpp b/perception/detected_object_validation/src/occupancy_grid_map/occupancy_grid_map_validator.hpp new file mode 100644 index 0000000000000..52b6f79e5e20a --- /dev/null +++ b/perception/detected_object_validation/src/occupancy_grid_map/occupancy_grid_map_validator.hpp @@ -0,0 +1,78 @@ +// Copyright 2022 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef OCCUPANCY_GRID_MAP__OCCUPANCY_GRID_MAP_VALIDATOR_HPP_ +#define OCCUPANCY_GRID_MAP__OCCUPANCY_GRID_MAP_VALIDATOR_HPP_ + +#include "autoware/universe_utils/ros/published_time_publisher.hpp" + +#include +#include +#include +#include + +#include "autoware_perception_msgs/msg/detected_objects.hpp" +#include + +#include +#include +#include +#include +#include + +namespace autoware::detected_object_validation +{ +namespace occupancy_grid_map +{ +class OccupancyGridBasedValidator : public rclcpp::Node +{ +public: + explicit OccupancyGridBasedValidator(const rclcpp::NodeOptions & node_options); + +private: + rclcpp::Publisher::SharedPtr objects_pub_; + message_filters::Subscriber objects_sub_; + message_filters::Subscriber occ_grid_sub_; + tf2_ros::Buffer tf_buffer_; + tf2_ros::TransformListener tf_listener_; + std::unique_ptr published_time_publisher_; + + typedef message_filters::sync_policies::ApproximateTime< + autoware_perception_msgs::msg::DetectedObjects, nav_msgs::msg::OccupancyGrid> + SyncPolicy; + typedef message_filters::Synchronizer Sync; + Sync sync_; + float mean_threshold_; + bool enable_debug_; + + void onObjectsAndOccGrid( + const autoware_perception_msgs::msg::DetectedObjects::ConstSharedPtr & input_objects, + const nav_msgs::msg::OccupancyGrid::ConstSharedPtr & input_occ_grid); + + cv::Mat fromOccupancyGrid(const nav_msgs::msg::OccupancyGrid & occupancy_grid); + std::optional getMask( + const nav_msgs::msg::OccupancyGrid & occupancy_grid, + const autoware_perception_msgs::msg::DetectedObject & object); + std::optional getMask( + const nav_msgs::msg::OccupancyGrid & occupancy_grid, + const autoware_perception_msgs::msg::DetectedObject & object, cv::Mat mask); + void showDebugImage( + const nav_msgs::msg::OccupancyGrid & ros_occ_grid, + const autoware_perception_msgs::msg::DetectedObjects & objects, const cv::Mat & occ_grid); +}; + +} // namespace occupancy_grid_map +} // namespace autoware::detected_object_validation + +#endif // OCCUPANCY_GRID_MAP__OCCUPANCY_GRID_MAP_VALIDATOR_HPP_ diff --git a/perception/detected_object_validation/src/position_filter/position_filter.cpp b/perception/detected_object_validation/src/position_filter/position_filter.cpp new file mode 100644 index 0000000000000..ac0bab404c476 --- /dev/null +++ b/perception/detected_object_validation/src/position_filter/position_filter.cpp @@ -0,0 +1,88 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "position_filter.hpp" + +namespace autoware::detected_object_validation +{ +namespace position_filter +{ +ObjectPositionFilterNode::ObjectPositionFilterNode(const rclcpp::NodeOptions & node_options) +: Node("object_position_filter_node", node_options), + tf_buffer_(this->get_clock()), + tf_listener_(tf_buffer_) +{ + using std::placeholders::_1; + + // Set parameters + upper_bound_x_ = declare_parameter("upper_bound_x", 100.0); + upper_bound_y_ = declare_parameter("upper_bound_y", 50.0); + lower_bound_x_ = declare_parameter("lower_bound_x", 0.0); + lower_bound_y_ = declare_parameter("lower_bound_y", -50.0); + filter_target_.UNKNOWN = declare_parameter("filter_target_label.UNKNOWN", false); + filter_target_.CAR = declare_parameter("filter_target_label.CAR", false); + filter_target_.TRUCK = declare_parameter("filter_target_label.TRUCK", false); + filter_target_.BUS = declare_parameter("filter_target_label.BUS", false); + filter_target_.TRAILER = declare_parameter("filter_target_label.TRAILER", false); + filter_target_.MOTORCYCLE = declare_parameter("filter_target_label.MOTORCYCLE", false); + filter_target_.BICYCLE = declare_parameter("filter_target_label.BICYCLE", false); + filter_target_.PEDESTRIAN = declare_parameter("filter_target_label.PEDESTRIAN", false); + + // Set publisher/subscriber + object_sub_ = this->create_subscription( + "input/object", rclcpp::QoS{1}, std::bind(&ObjectPositionFilterNode::objectCallback, this, _1)); + object_pub_ = this->create_publisher( + "output/object", rclcpp::QoS{1}); + published_time_publisher_ = + std::make_unique(this); +} + +void ObjectPositionFilterNode::objectCallback( + const autoware_perception_msgs::msg::DetectedObjects::ConstSharedPtr input_msg) +{ + // Guard + if (object_pub_->get_subscription_count() < 1) return; + + autoware_perception_msgs::msg::DetectedObjects output_object_msg; + output_object_msg.header = input_msg->header; + + for (const auto & object : input_msg->objects) { + const auto & label = object.classification.front().label; + if (filter_target_.isTarget(label)) { + if (isObjectInBounds(object)) { + output_object_msg.objects.emplace_back(object); + } + } else { + output_object_msg.objects.emplace_back(object); + } + } + + object_pub_->publish(output_object_msg); + published_time_publisher_->publish_if_subscribed(object_pub_, output_object_msg.header.stamp); +} + +bool ObjectPositionFilterNode::isObjectInBounds( + const autoware_perception_msgs::msg::DetectedObject & object) const +{ + const auto & position = object.kinematics.pose_with_covariance.pose.position; + return position.x > lower_bound_x_ && position.x < upper_bound_x_ && + position.y > lower_bound_y_ && position.y < upper_bound_y_; +} + +} // namespace position_filter +} // namespace autoware::detected_object_validation + +#include +RCLCPP_COMPONENTS_REGISTER_NODE( + autoware::detected_object_validation::position_filter::ObjectPositionFilterNode) diff --git a/perception/detected_object_validation/src/position_filter/position_filter.hpp b/perception/detected_object_validation/src/position_filter/position_filter.hpp new file mode 100644 index 0000000000000..30a5d7fc9dfb1 --- /dev/null +++ b/perception/detected_object_validation/src/position_filter/position_filter.hpp @@ -0,0 +1,65 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef POSITION_FILTER__POSITION_FILTER_HPP_ +#define POSITION_FILTER__POSITION_FILTER_HPP_ + +#include "autoware/detected_object_validation/utils/utils.hpp" +#include "autoware/universe_utils/geometry/geometry.hpp" +#include "autoware/universe_utils/ros/published_time_publisher.hpp" + +#include + +#include "autoware_map_msgs/msg/lanelet_map_bin.hpp" +#include "autoware_perception_msgs/msg/detected_objects.hpp" + +#include +#include + +#include +#include + +namespace autoware::detected_object_validation +{ +namespace position_filter +{ + +class ObjectPositionFilterNode : public rclcpp::Node +{ +public: + explicit ObjectPositionFilterNode(const rclcpp::NodeOptions & node_options); + +private: + void objectCallback(const autoware_perception_msgs::msg::DetectedObjects::ConstSharedPtr); + + rclcpp::Publisher::SharedPtr object_pub_; + rclcpp::Subscription::SharedPtr object_sub_; + + tf2_ros::Buffer tf_buffer_; + tf2_ros::TransformListener tf_listener_; + + float upper_bound_x_; + float upper_bound_y_; + float lower_bound_x_; + float lower_bound_y_; + utils::FilterTargetLabel filter_target_; + bool isObjectInBounds(const autoware_perception_msgs::msg::DetectedObject & object) const; + + std::unique_ptr published_time_publisher_; +}; + +} // namespace position_filter +} // namespace autoware::detected_object_validation + +#endif // POSITION_FILTER__POSITION_FILTER_HPP_ diff --git a/perception/ground_segmentation/src/ransac_ground_filter/node.cpp b/perception/ground_segmentation/src/ransac_ground_filter/node.cpp new file mode 100644 index 0000000000000..774c19e866357 --- /dev/null +++ b/perception/ground_segmentation/src/ransac_ground_filter/node.cpp @@ -0,0 +1,409 @@ +// Copyright 2020 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "node.hpp" + +#include + +#include + +#include +#include +#include +#include + +namespace +{ +Eigen::Vector3d getArbitraryOrthogonalVector(const Eigen::Vector3d & input) +{ + const double x = input.x(); + const double y = input.y(); + const double z = input.z(); + const double x2 = std::pow(x, 2); + const double y2 = std::pow(y, 2); + const double z2 = std::pow(z, 2); + + Eigen::Vector3d unit_vec{0, 0, 0}; + if (x2 <= y2 && x2 <= z2) { + unit_vec.x() = 0; + unit_vec.y() = z; + unit_vec.z() = -y; + unit_vec = unit_vec / std::sqrt(y2 + z2); + } else if (y2 <= x2 && y2 <= z2) { + unit_vec.x() = -z; + unit_vec.y() = 0; + unit_vec.z() = x; + unit_vec = unit_vec / std::sqrt(z2 + x2); + } else if (z2 <= x2 && z2 <= y2) { + unit_vec.x() = y; + unit_vec.y() = -x; + unit_vec.z() = 0; + unit_vec = unit_vec / std::sqrt(x2 + y2); + } + return unit_vec; +} + +geometry_msgs::msg::Pose getDebugPose(const Eigen::Affine3d & plane_affine) +{ + geometry_msgs::msg::Pose debug_pose; + const Eigen::Quaterniond quat(plane_affine.rotation()); + debug_pose.position.x = plane_affine.translation().x(); + debug_pose.position.y = plane_affine.translation().y(); + debug_pose.position.z = plane_affine.translation().z(); + debug_pose.orientation.x = quat.x(); + debug_pose.orientation.y = quat.y(); + debug_pose.orientation.z = quat.z(); + debug_pose.orientation.w = quat.w(); + return debug_pose; +} +} // namespace + +namespace autoware::ground_segmentation +{ +PlaneBasis getPlaneBasis(const Eigen::Vector3d & plane_normal) +{ + PlaneBasis basis; + basis.e_z = plane_normal; + basis.e_x = getArbitraryOrthogonalVector(plane_normal); + basis.e_y = basis.e_x.cross(basis.e_z); + return basis; +} + +< < < < < < < < HEAD : perception / ground_segmentation / src / ransac_ground_filter / + node.cpp using pointcloud_preprocessor::get_param; +== == == == using autoware::pointcloud_preprocessor::get_param; +>>>>>>>> original / + main +: perception / + autoware_ground_segmentation / src / ransac_ground_filter / + node.cpp + + RANSACGroundFilterComponent::RANSACGroundFilterComponent(const rclcpp::NodeOptions & options) +: Filter("RANSACGroundFilter", options) +{ + base_frame_ = declare_parameter("base_frame", "base_link"); + unit_axis_ = declare_parameter("unit_axis"); + max_iterations_ = declare_parameter("max_iterations"); + min_inliers_ = declare_parameter("min_trial"); + min_points_ = declare_parameter("min_points"); + outlier_threshold_ = declare_parameter("outlier_threshold"); + plane_slope_threshold_ = declare_parameter("plane_slope_threshold"); + voxel_size_x_ = declare_parameter("voxel_size_x"); + voxel_size_y_ = declare_parameter("voxel_size_y"); + voxel_size_z_ = declare_parameter("voxel_size_z"); + height_threshold_ = declare_parameter("height_threshold"); + debug_ = declare_parameter("debug"); + + if (unit_axis_ == "x") { + unit_vec_ = Eigen::Vector3d::UnitX(); + } else if (unit_axis_ == "y") { + unit_vec_ = Eigen::Vector3d::UnitY(); + } else { // including (unit_axis_ == "z") + unit_vec_ = Eigen::Vector3d::UnitZ(); + } + + if (debug_) { + setDebugPublisher(); + } + + using std::placeholders::_1; + set_param_res_ = this->add_on_set_parameters_callback( + std::bind(&RANSACGroundFilterComponent::paramCallback, this, _1)); + + pcl::console::setVerbosityLevel(pcl::console::L_ALWAYS); +} + +void RANSACGroundFilterComponent::setDebugPublisher() +{ + if (is_initialized_debug_message_) { + return; + } + debug_pose_array_pub_ = + create_publisher("debug/plane_pose_array", max_queue_size_); + debug_ground_cloud_pub_ = + create_publisher("debug/ground/pointcloud", max_queue_size_); + is_initialized_debug_message_ = true; +} + +void RANSACGroundFilterComponent::publishDebugMessage( + const geometry_msgs::msg::PoseArray & debug_pose_array, + const pcl::PointCloud & ground_cloud, const std_msgs::msg::Header & header) +{ + pcl::PointCloud::Ptr colored_ground_ptr(new pcl::PointCloud); + for (const auto & ground_point : ground_cloud.points) { + pcl::PointXYZRGB p; + p.x = ground_point.x; + p.y = ground_point.y; + p.z = ground_point.z; + p.r = 255.0; + p.g = 0.0; + p.b = 0.0; + colored_ground_ptr->points.push_back(p); + } + sensor_msgs::msg::PointCloud2::SharedPtr ground_cloud_msg_ptr(new sensor_msgs::msg::PointCloud2); + ground_cloud_msg_ptr->header = header; + pcl::toROSMsg(*colored_ground_ptr, *ground_cloud_msg_ptr); + ground_cloud_msg_ptr->header.frame_id = base_frame_; + debug_pose_array_pub_->publish(debug_pose_array); + debug_ground_cloud_pub_->publish(*ground_cloud_msg_ptr); +} + +bool RANSACGroundFilterComponent::transformPointCloud( + const std::string & in_target_frame, const PointCloud2ConstPtr & in_cloud_ptr, + const PointCloud2::SharedPtr & out_cloud_ptr) +{ + if (in_target_frame == in_cloud_ptr->header.frame_id) { + *out_cloud_ptr = *in_cloud_ptr; + return true; + } + + geometry_msgs::msg::TransformStamped transform_stamped; + try { + transform_stamped = tf_buffer_->lookupTransform( + in_target_frame, in_cloud_ptr->header.frame_id, in_cloud_ptr->header.stamp, + rclcpp::Duration::from_seconds(1.0)); + } catch (tf2::TransformException & ex) { + RCLCPP_WARN(this->get_logger(), "%s", ex.what()); + return false; + } + // tf2::doTransform(*in_cloud_ptr, *out_cloud_ptr, transform_stamped); + Eigen::Matrix4f mat = tf2::transformToEigen(transform_stamped.transform).matrix().cast(); + pcl_ros::transformPointCloud(mat, *in_cloud_ptr, *out_cloud_ptr); + out_cloud_ptr->header.frame_id = in_target_frame; + return true; +} + +void RANSACGroundFilterComponent::extractPointsIndices( + const pcl::PointCloud::Ptr in_cloud_ptr, const pcl::PointIndices & in_indices, + pcl::PointCloud::Ptr out_only_indices_cloud_ptr, + pcl::PointCloud::Ptr out_removed_indices_cloud_ptr) +{ + pcl::ExtractIndices extract_ground; + extract_ground.setInputCloud(in_cloud_ptr); + extract_ground.setIndices(pcl::make_shared(in_indices)); + + extract_ground.setNegative(false); // true removes the indices, false leaves only the indices + extract_ground.filter(*out_only_indices_cloud_ptr); + + extract_ground.setNegative(true); // true removes the indices, false leaves only the indices + extract_ground.filter(*out_removed_indices_cloud_ptr); +} + +Eigen::Affine3d RANSACGroundFilterComponent::getPlaneAffine( + const pcl::PointCloud segment_ground_cloud, const Eigen::Vector3d & plane_normal) +{ + pcl::CentroidPoint centroid; + for (const auto p : segment_ground_cloud.points) { + centroid.add(p); + } + pcl::PointXYZ centroid_point; + centroid.get(centroid_point); + Eigen::Translation trans(centroid_point.x, centroid_point.y, centroid_point.z); + const PlaneBasis basis = getPlaneBasis(plane_normal); + Eigen::Matrix3d rot; + rot << basis.e_x.x(), basis.e_y.x(), basis.e_z.x(), basis.e_x.y(), basis.e_y.y(), basis.e_z.y(), + basis.e_x.z(), basis.e_y.z(), basis.e_z.z(); + return trans * rot; +} + +void RANSACGroundFilterComponent::applyRANSAC( + const pcl::PointCloud::Ptr & input, pcl::PointIndices::Ptr & output_inliers, + pcl::ModelCoefficients::Ptr & output_coefficients) +{ + pcl::SACSegmentation seg; + seg.setOptimizeCoefficients(true); + seg.setRadiusLimits(0.3, std::numeric_limits::max()); + seg.setMethodType(pcl::SAC_RANSAC); + seg.setDistanceThreshold(outlier_threshold_); + seg.setInputCloud(input); + seg.setMaxIterations(max_iterations_); + seg.setModelType(pcl::SACMODEL_PLANE); + seg.segment(*output_inliers, *output_coefficients); +} + +void RANSACGroundFilterComponent::filter( + const PointCloud2::ConstSharedPtr & input, [[maybe_unused]] const IndicesPtr & indices, + PointCloud2 & output) +{ + std::scoped_lock lock(mutex_); + sensor_msgs::msg::PointCloud2::SharedPtr input_transformed_ptr(new sensor_msgs::msg::PointCloud2); + if (!transformPointCloud(base_frame_, input, input_transformed_ptr)) { + RCLCPP_ERROR_STREAM_THROTTLE( + this->get_logger(), *this->get_clock(), std::chrono::milliseconds(1000).count(), + "Failed transform from " << base_frame_ << " to " << input->header.frame_id); + return; + } + pcl::PointCloud::Ptr current_sensor_cloud_ptr(new pcl::PointCloud); + pcl::fromROSMsg(*input_transformed_ptr, *current_sensor_cloud_ptr); + + // downsample pointcloud to reduce ransac calculation cost + pcl::PointCloud::Ptr downsampled_cloud(new pcl::PointCloud); + downsampled_cloud->points.reserve(current_sensor_cloud_ptr->points.size()); + pcl::VoxelGrid filter; + filter.setInputCloud(current_sensor_cloud_ptr); + filter.setLeafSize(voxel_size_x_, voxel_size_y_, voxel_size_z_); + filter.filter(*downsampled_cloud); + + // apply ransac + pcl::PointIndices::Ptr inliers(new pcl::PointIndices); + pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients); + applyRANSAC(downsampled_cloud, inliers, coefficients); + + if (coefficients->values.empty()) { + RCLCPP_WARN_THROTTLE( + this->get_logger(), *this->get_clock(), std::chrono::milliseconds(1000).count(), + "failed to find a plane"); + output = *input; + return; + } + + // filter too tilt plane to avoid mis-fitting (e.g. fitting to wall plane) + Eigen::Vector3d plane_normal( + coefficients->values[0], coefficients->values[1], coefficients->values[2]); + { + const auto plane_slope = std::abs( + std::acos(plane_normal.dot(unit_vec_) / (plane_normal.norm() * unit_vec_.norm())) * 180 / + M_PI); + if (plane_slope > plane_slope_threshold_) { + output = *input; + return; + } + } + + // extract pointcloud from indices + pcl::PointCloud::Ptr segment_ground_cloud_ptr(new pcl::PointCloud); + pcl::PointCloud::Ptr segment_no_ground_cloud_ptr(new pcl::PointCloud); + extractPointsIndices( + downsampled_cloud, *inliers, segment_ground_cloud_ptr, segment_no_ground_cloud_ptr); + const Eigen::Affine3d plane_affine = getPlaneAffine(*segment_ground_cloud_ptr, plane_normal); + pcl::PointCloud::Ptr no_ground_cloud_ptr(new pcl::PointCloud); + + int x_offset = input->fields[pcl::getFieldIndex(*input, "x")].offset; + int y_offset = input->fields[pcl::getFieldIndex(*input, "y")].offset; + int z_offset = input->fields[pcl::getFieldIndex(*input, "z")].offset; + int point_step = input->point_step; + + sensor_msgs::msg::PointCloud2::SharedPtr no_ground_cloud_msg_ptr( + new sensor_msgs::msg::PointCloud2); + no_ground_cloud_msg_ptr->header = input->header; + no_ground_cloud_msg_ptr->fields = input->fields; + no_ground_cloud_msg_ptr->point_step = point_step; + no_ground_cloud_msg_ptr->data.resize(input->data.size()); + size_t output_size = 0; + // use not downsampled pointcloud for extract pointcloud that higher than height threshold + for (size_t global_size = 0; global_size < input->data.size(); global_size += point_step) { + float x = *reinterpret_cast(&input->data[global_size + x_offset]); + float y = *reinterpret_cast(&input->data[global_size + y_offset]); + float z = *reinterpret_cast(&input->data[global_size + z_offset]); + const Eigen::Vector3d transformed_point = plane_affine.inverse() * Eigen::Vector3d(x, y, z); + if (std::abs(transformed_point.z()) > height_threshold_) { + std::memcpy( + &no_ground_cloud_msg_ptr->data[output_size], &input->data[global_size], point_step); + output_size += point_step; + } + } + no_ground_cloud_msg_ptr->data.resize(output_size); + no_ground_cloud_msg_ptr->height = input->height; + no_ground_cloud_msg_ptr->width = output_size / point_step / input->height; + no_ground_cloud_msg_ptr->row_step = output_size / input->height; + no_ground_cloud_msg_ptr->is_dense = input->is_dense; + no_ground_cloud_msg_ptr->is_bigendian = input->is_bigendian; + + sensor_msgs::msg::PointCloud2::SharedPtr no_ground_cloud_transformed_msg_ptr( + new sensor_msgs::msg::PointCloud2); + if (!transformPointCloud( + base_frame_, no_ground_cloud_msg_ptr, no_ground_cloud_transformed_msg_ptr)) { + RCLCPP_ERROR_STREAM_THROTTLE( + this->get_logger(), *this->get_clock(), std::chrono::milliseconds(1000).count(), + "Failed transform from " << base_frame_ << " to " + << no_ground_cloud_msg_ptr->header.frame_id); + return; + } + output = *no_ground_cloud_transformed_msg_ptr; + + // output debug plane coords and ground pointcloud when debug flag is set + if (debug_) { + const geometry_msgs::msg::Pose debug_pose = getDebugPose(plane_affine); + geometry_msgs::msg::PoseArray debug_pose_array; + debug_pose_array.header.frame_id = base_frame_; + debug_pose_array.poses.push_back(debug_pose); + publishDebugMessage(debug_pose_array, *segment_ground_cloud_ptr, input->header); + } +} + +rcl_interfaces::msg::SetParametersResult RANSACGroundFilterComponent::paramCallback( + const std::vector & p) +{ + std::scoped_lock lock(mutex_); + + if (get_param(p, "base_frame", base_frame_)) { + RCLCPP_DEBUG(get_logger(), "Setting base_frame to: %s.", base_frame_.c_str()); + } + if (get_param(p, "max_iterations", max_iterations_)) { + RCLCPP_DEBUG(get_logger(), "Setting max_iterations to: %d.", max_iterations_); + } + if (get_param(p, "min_inliers", min_inliers_)) { + RCLCPP_DEBUG(get_logger(), "Setting min_inliers to: %d.", min_inliers_); + } + if (get_param(p, "min_points", min_points_)) { + RCLCPP_DEBUG(get_logger(), "Setting min_points to: %d.", min_points_); + } + if (get_param(p, "outlier_threshold", outlier_threshold_)) { + RCLCPP_DEBUG(get_logger(), "Setting outlier_threshold to: %lf.", outlier_threshold_); + } + if (get_param(p, "height_threshold", height_threshold_)) { + RCLCPP_DEBUG(get_logger(), "Setting height_threshold_ to: %lf.", height_threshold_); + } + if (get_param(p, "plane_slope_threshold", plane_slope_threshold_)) { + RCLCPP_DEBUG(get_logger(), "Setting plane_slope_threshold to: %lf.", plane_slope_threshold_); + } + if (get_param(p, "voxel_size_x", voxel_size_x_)) { + RCLCPP_DEBUG(get_logger(), "Setting voxel_size_x to: %lf.", voxel_size_x_); + } + if (get_param(p, "voxel_size_y", voxel_size_y_)) { + RCLCPP_DEBUG(get_logger(), "Setting voxel_size_y to: %lf.", voxel_size_y_); + } + if (get_param(p, "voxel_size_z", voxel_size_z_)) { + RCLCPP_DEBUG(get_logger(), "Setting voxel_size_z to: %lf.", voxel_size_z_); + } + if (get_param(p, "debug", debug_)) { + RCLCPP_DEBUG(get_logger(), "Setting debug to: %d.", debug_); + } + + if (debug_) { + setDebugPublisher(); + } + + if (get_param(p, "unit_axis", unit_axis_)) { + if (unit_axis_ == "x") { + unit_vec_ = Eigen::Vector3d::UnitX(); + } else if (unit_axis_ == "y") { + unit_vec_ = Eigen::Vector3d::UnitY(); + } else { // including (unit_axis_ == "z") + unit_vec_ = Eigen::Vector3d::UnitZ(); + } + RCLCPP_DEBUG(get_logger(), "Setting unit_axis to: %s.", unit_axis_.c_str()); + } + + rcl_interfaces::msg::SetParametersResult result; + result.successful = true; + result.reason = "success"; + + return result; +} + +} // namespace autoware::ground_segmentation + +#include +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::ground_segmentation::RANSACGroundFilterComponent) diff --git a/perception/ground_segmentation/src/ransac_ground_filter/node.hpp b/perception/ground_segmentation/src/ransac_ground_filter/node.hpp new file mode 100644 index 0000000000000..e173fe131602f --- /dev/null +++ b/perception/ground_segmentation/src/ransac_ground_filter/node.hpp @@ -0,0 +1,134 @@ +// Copyright 2020 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef RANSAC_GROUND_FILTER__NODE_HPP_ +#define RANSAC_GROUND_FILTER__NODE_HPP_ + +#include "autoware/pointcloud_preprocessor/filter.hpp" + +#include +#include + +#include +#include +#include +#include +#include + +#ifdef ROS_DISTRO_GALACTIC +#include +#else +#include +#endif + +#include + +#include +#include +#include + +namespace autoware::ground_segmentation +{ +struct PlaneBasis +{ + Eigen::Vector3d e_x; + Eigen::Vector3d e_y; + Eigen::Vector3d e_z; +}; + +struct RGB +{ + double r = 0.0; + double g = 0.0; + double b = 0.0; +}; + +class RANSACGroundFilterComponent : public autoware::pointcloud_preprocessor::Filter +{ + using PointType = pcl::PointXYZ; + +protected: + void filter( + const PointCloud2ConstPtr & input, const IndicesPtr & indices, PointCloud2 & output) override; + +private: + rclcpp::Publisher::SharedPtr debug_pose_array_pub_; + rclcpp::Publisher::SharedPtr debug_ground_cloud_pub_; + + std::string base_frame_ = "base_link"; + std::string unit_axis_ = "z"; + int max_iterations_ = 0; + int min_inliers_ = 0; + int min_points_ = 0; + double outlier_threshold_ = 0.1; + double plane_slope_threshold_ = 10.0; + double height_threshold_ = 0.1; + double voxel_size_x_ = 0.1; + double voxel_size_y_ = 0.1; + double voxel_size_z_ = 0.1; + bool debug_ = false; + bool is_initialized_debug_message_ = false; + Eigen::Vector3d unit_vec_ = Eigen::Vector3d::UnitZ(); + + /*! + * Output transformed PointCloud from in_cloud_ptr->header.frame_id to in_target_frame + * @param[in] in_target_frame Coordinate system to perform transform + * @param[in] in_cloud_ptr PointCloud to perform transform + * @param[out] out_cloud_ptr Resulting transformed PointCloud + * @retval true transform succeeded + * @retval false transform failed + */ + bool transformPointCloud( + const std::string & in_target_frame, const PointCloud2ConstPtr & in_cloud_ptr, + const PointCloud2::SharedPtr & out_cloud_ptr); + + /*! + * Returns the resulting complementary PointCloud, one with the points kept and the other removed + * as indicated in the indices + * @param in_cloud_ptr Input PointCloud to which the extraction will be performed + * @param in_indices Indices of the points to be both removed and kept + * @param out_only_indices_cloud_ptr Resulting PointCloud with the indices kept + * @param out_removed_indices_cloud_ptr Resulting PointCloud with the indices removed + */ + void extractPointsIndices( + const pcl::PointCloud::Ptr in_cloud_ptr, const pcl::PointIndices & in_indices, + pcl::PointCloud::Ptr out_only_indices_cloud_ptr, + pcl::PointCloud::Ptr out_removed_indices_cloud_ptr); + + Eigen::Affine3d getPlaneAffine( + const pcl::PointCloud segment_ground_cloud, const Eigen::Vector3d & plane_normal); + + void applyRANSAC( + const pcl::PointCloud::Ptr & input, pcl::PointIndices::Ptr & output_inliers, + pcl::ModelCoefficients::Ptr & output_coefficients); + + void publishDebugMessage( + const geometry_msgs::msg::PoseArray & debug_pose_array, + const pcl::PointCloud & ground_cloud, const std_msgs::msg::Header & header); + + void setDebugPublisher(); + + /** \brief Parameter service callback result : needed to be hold */ + OnSetParametersCallbackHandle::SharedPtr set_param_res_; + + /** \brief Parameter service callback */ + rcl_interfaces::msg::SetParametersResult paramCallback(const std::vector & p); + +public: + EIGEN_MAKE_ALIGNED_OPERATOR_NEW + explicit RANSACGroundFilterComponent(const rclcpp::NodeOptions & options); +}; +} // namespace autoware::ground_segmentation + +#endif // RANSAC_GROUND_FILTER__NODE_HPP_ diff --git a/perception/ground_segmentation/src/ray_ground_filter/gencolors.hpp b/perception/ground_segmentation/src/ray_ground_filter/gencolors.hpp new file mode 100644 index 0000000000000..658e5718a01cc --- /dev/null +++ b/perception/ground_segmentation/src/ray_ground_filter/gencolors.hpp @@ -0,0 +1,162 @@ +// Copyright 2020 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef RAY_GROUND_FILTER__GENCOLORS_HPP_ +#define RAY_GROUND_FILTER__GENCOLORS_HPP_ + +#include +#include + +#include + +#include +#include + +namespace autoware::ray_ground_filter +{ +using namespace cv; // NOLINT + +inline static void downsamplePoints(const Mat & src, Mat & dst, size_t count) +{ + CV_Assert(count >= 2); + CV_Assert(src.cols == 1 || src.rows == 1); + CV_Assert(src.total() >= count); + CV_Assert(src.type() == CV_8UC3); + + dst.create(1, static_cast(count), CV_8UC3); + // TODO(YamatoAndo): optimize by exploiting symmetry in the distance matrix + Mat dists(static_cast(src.total()), static_cast(src.total()), CV_32FC1, Scalar(0)); + if (dists.empty()) { + std::cerr << "Such big matrix can't be created." << std::endl; + } + + for (int i = 0; i < dists.rows; i++) { + for (int j = i; j < dists.cols; j++) { + float dist = static_cast(norm(src.at>(i) - src.at>(j))); + dists.at(j, i) = dists.at(i, j) = dist; + } + } + + double maxVal; + Point maxLoc; + minMaxLoc(dists, 0, &maxVal, 0, &maxLoc); + + dst.at>(0) = src.at>(maxLoc.x); + dst.at>(1) = src.at>(maxLoc.y); + + // ignore cspell error due to the source from OpenCV + // cspell: ignore actived Dists randu + Mat activedDists(0, dists.cols, dists.type()); + Mat candidatePointsMask(1, dists.cols, CV_8UC1, Scalar(255)); + activedDists.push_back(dists.row(maxLoc.y)); + candidatePointsMask.at(0, maxLoc.y) = 0; + + for (size_t i = 2; i < count; i++) { + activedDists.push_back(dists.row(maxLoc.x)); + candidatePointsMask.at(0, maxLoc.x) = 0; + + Mat minDists; + reduce(activedDists, minDists, 0, CV_REDUCE_MIN); + minMaxLoc(minDists, 0, &maxVal, 0, &maxLoc, candidatePointsMask); + dst.at>(static_cast(i)) = src.at>(maxLoc.x); + } +} + +inline void generateColors(std::vector & colors, size_t count, size_t factor = 100) +{ + if (count < 1) { + return; + } + + colors.resize(count); + + if (count == 1) { + colors[0] = Scalar(0, 0, 255); // red + return; + } + if (count == 2) { + colors[0] = Scalar(0, 0, 255); // red + colors[1] = Scalar(0, 255, 0); // green + return; + } + + // Generate a set of colors in RGB space. A size of the set is several times (=factor) larger then + // the needed count of colors. + Mat bgr(1, static_cast(count * factor), CV_8UC3); + randu(bgr, 0, 256); + + // Convert the colors set to Lab space. + // Distances between colors in this space correspond a human perception. + Mat lab; + cvtColor(bgr, lab, cv::COLOR_BGR2Lab); + + // Subsample colors from the generated set so that + // to maximize the minimum distances between each other. + // Douglas-Peucker algorithm is used for this. + Mat lab_subset; + downsamplePoints(lab, lab_subset, count); + + // Convert subsampled colors back to RGB + Mat bgr_subset; + cvtColor(lab_subset, bgr_subset, cv::COLOR_BGR2Lab); + + CV_Assert(bgr_subset.total() == count); + for (size_t i = 0; i < count; i++) { + Point3_ c = bgr_subset.at>(static_cast(i)); + colors[i] = Scalar(c.x, c.y, c.z); + } +} +} // namespace autoware::ray_ground_filter +#endif // RAY_GROUND_FILTER__GENCOLORS_HPP_ diff --git a/perception/ground_segmentation/src/ray_ground_filter/node.cpp b/perception/ground_segmentation/src/ray_ground_filter/node.cpp new file mode 100644 index 0000000000000..fd8fcd4dc29e7 --- /dev/null +++ b/perception/ground_segmentation/src/ray_ground_filter/node.cpp @@ -0,0 +1,402 @@ +// Copyright 2020 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +/* + * Copyright 2017-2019 Autoware Foundation. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + ******************** + * v1.0: amc-nu (abrahammonrroy@yahoo.com) + */ + +#include "node.hpp" + +#include +#include + +namespace autoware::ground_segmentation +{ +using autoware::pointcloud_preprocessor::get_param; + +RayGroundFilterComponent::RayGroundFilterComponent(const rclcpp::NodeOptions & options) +: Filter("RayGroundFilter", options) +{ + // set initial parameters + { + grid_width_ = 1000; + grid_height_ = 1000; + grid_precision_ = 0.2; + ray_ground_filter::generateColors(colors_, color_num_); + + min_x_ = declare_parameter("min_x"); + max_x_ = declare_parameter("max_x"); + min_y_ = declare_parameter("min_y"); + max_y_ = declare_parameter("max_y"); + + setVehicleFootprint(min_x_, max_x_, min_y_, max_y_); + + use_vehicle_footprint_ = declare_parameter("use_vehicle_footprint", false); + + general_max_slope_ = declare_parameter("general_max_slope"); + local_max_slope_ = declare_parameter("local_max_slope"); + initial_max_slope_ = declare_parameter("initial_max_slope"); + radial_divider_angle_ = declare_parameter("radial_divider_angle"); + min_height_threshold_ = declare_parameter("min_height_threshold"); + concentric_divider_distance_ = declare_parameter("concentric_divider_distance"); + reclass_distance_threshold_ = declare_parameter("reclass_distance_threshold"); + } + + using std::placeholders::_1; + set_param_res_ = this->add_on_set_parameters_callback( + std::bind(&RayGroundFilterComponent::paramCallback, this, _1)); +} + +void RayGroundFilterComponent::ConvertXYZIToRTZColor( + const pcl::PointCloud::Ptr in_cloud, PointCloudXYZRTColor & out_organized_points, + std::vector & out_radial_divided_indices, + std::vector & out_radial_ordered_clouds) +{ + out_organized_points.resize(in_cloud->points.size()); + out_radial_divided_indices.clear(); + out_radial_divided_indices.resize(radial_dividers_num_); + out_radial_ordered_clouds.resize(radial_dividers_num_); + + for (size_t i = 0; i < in_cloud->points.size(); i++) { + PointXYZRTColor new_point; + auto radius = static_cast(sqrt( + in_cloud->points[i].x * in_cloud->points[i].x + + in_cloud->points[i].y * in_cloud->points[i].y)); + auto theta = + static_cast(atan2(in_cloud->points[i].y, in_cloud->points[i].x)) * 180 / M_PI; + if (theta < 0) { + theta += 360; + } + if (theta >= 360) { + theta -= 360; + } + auto radial_div = static_cast(floor(theta / radial_divider_angle_)); + + new_point.point.x = in_cloud->points[i].x; + new_point.point.y = in_cloud->points[i].y; + new_point.point.z = in_cloud->points[i].z; + // new_point.ring = in_cloud->points[i].ring; + new_point.radius = radius; + new_point.theta = theta; + new_point.radial_div = radial_div; + new_point.red = static_cast(colors_[new_point.radial_div % color_num_].val[0]); + new_point.green = static_cast(colors_[new_point.radial_div % color_num_].val[1]); + new_point.blue = static_cast(colors_[new_point.radial_div % color_num_].val[2]); + new_point.original_index = i; + + out_organized_points[i] = new_point; + + // radial divisions + out_radial_divided_indices[radial_div].indices.push_back(i); + + out_radial_ordered_clouds[radial_div].push_back(new_point); + } // end for + + // order radial points on each division +#pragma omp for + for (size_t i = 0; i < radial_dividers_num_; i++) { + std::sort( + out_radial_ordered_clouds[i].begin(), out_radial_ordered_clouds[i].end(), + [](const PointXYZRTColor & a, const PointXYZRTColor & b) { return a.radius < b.radius; }); + } +} + +boost::optional RayGroundFilterComponent::calcPointVehicleIntersection(const Point & point) +{ + bg::model::linestring ls = {{0.0, 0.0}, point}; + std::vector collision_points; + bg::intersection(ls, vehicle_footprint_, collision_points); + + if (collision_points.size() < 1) { + return {}; + } + return bg::distance(Point(0, 0), collision_points.front()); +} + +void RayGroundFilterComponent::setVehicleFootprint( + const double min_x, const double max_x, const double min_y, const double max_y) +{ + // create vehicle footprint polygon + vehicle_footprint_.outer().clear(); + vehicle_footprint_.outer().push_back(Point(min_x, min_y)); // left back + vehicle_footprint_.outer().push_back(Point(min_x, max_y)); // right back + vehicle_footprint_.outer().push_back(Point(max_x, max_y)); // right front + vehicle_footprint_.outer().push_back(Point(max_x, min_y)); // left front + vehicle_footprint_.outer().push_back(Point(min_x, min_y)); // left back +} + +void RayGroundFilterComponent::ClassifyPointCloud( + std::vector & in_radial_ordered_clouds, + pcl::PointIndices & out_ground_indices, pcl::PointIndices & out_no_ground_indices) +{ + out_ground_indices.indices.clear(); + out_no_ground_indices.indices.clear(); +#pragma omp for + for (size_t i = 0; i < in_radial_ordered_clouds.size(); + i++) // sweep through each radial division + { + float prev_radius = 0.f; + float prev_height = 0.f; + bool prev_ground = false; + bool current_ground = false; + for (size_t j = 0; j < in_radial_ordered_clouds[i].size(); + j++) // loop through each point in the radial div + { + double local_max_slope = local_max_slope_; + if (j == 0) { + local_max_slope = initial_max_slope_; + if (use_vehicle_footprint_) { + // calc intersection of vehicle footprint and initial point vector + const auto radius = calcPointVehicleIntersection( + Point{in_radial_ordered_clouds[i][j].point.x, in_radial_ordered_clouds[i][j].point.y}); + if (radius) { + prev_radius = *radius; + } else { + // This case may happen if point was detected inside vehicle footprint for example + // RCLCPP_ERROR( + // this->get_logger(), + // "failed to find intersection of initial point line and vehicle footprint"); + continue; + } + } + } + + float points_distance = in_radial_ordered_clouds[i][j].radius - prev_radius; + float height_threshold = tan(DEG2RAD(local_max_slope)) * points_distance; + float current_height = in_radial_ordered_clouds[i][j].point.z; + float general_height_threshold = + tan(DEG2RAD(general_max_slope_)) * in_radial_ordered_clouds[i][j].radius; + + // for points which are very close causing the height threshold to be tiny, + // set a minimum value + if (height_threshold < min_height_threshold_) { + height_threshold = min_height_threshold_; + } + // only check points which radius is larger than the concentric_divider + if (points_distance < concentric_divider_distance_) { + current_ground = prev_ground; + } else { + // check current point height against the LOCAL threshold (previous point) + if ( + current_height <= (prev_height + height_threshold) && + current_height >= (prev_height - height_threshold)) { + // Check again using general geometry (radius from origin) + // if previous points wasn't ground + if (!prev_ground) { + if ( + current_height <= general_height_threshold && + current_height >= -general_height_threshold) { + current_ground = true; + } else { + current_ground = false; + } + } else { + current_ground = true; + } + } else { + // check if previous point is too far from previous one, if so classify again + if ( + points_distance > reclass_distance_threshold_ && + (current_height <= general_height_threshold && + current_height >= -general_height_threshold)) { + current_ground = true; + } else { + current_ground = false; + } + } + } // end larger than concentric_divider + + if (current_ground) { + out_ground_indices.indices.push_back(in_radial_ordered_clouds[i][j].original_index); + prev_ground = true; + } else { + out_no_ground_indices.indices.push_back(in_radial_ordered_clouds[i][j].original_index); + prev_ground = false; + } + + prev_radius = in_radial_ordered_clouds[i][j].radius; + prev_height = in_radial_ordered_clouds[i][j].point.z; + } + } +} + +// [ROS 2-port]: removed +// bool RayGroundFilterComponent::child_init() +// { +// // Enable the dynamic reconfigure service +// has_service = true; +// srv_ = boost::make_shared< +// dynamic_reconfigure::Server >(nh); +// dynamic_reconfigure::Server::CallbackType +// f = +// boost::bind(&RayGroundFilterComponent::config_callback, this, _1, _2); +// srv_->setCallback(f); +// return (true); +// } + +void RayGroundFilterComponent::initializePointCloud2( + const PointCloud2::ConstSharedPtr & in_cloud_ptr, + const PointCloud2::SharedPtr & out_cloud_msg_ptr) +{ + out_cloud_msg_ptr->header = in_cloud_ptr->header; + out_cloud_msg_ptr->height = in_cloud_ptr->height; + out_cloud_msg_ptr->fields = in_cloud_ptr->fields; + out_cloud_msg_ptr->is_bigendian = in_cloud_ptr->is_bigendian; + out_cloud_msg_ptr->point_step = in_cloud_ptr->point_step; + out_cloud_msg_ptr->is_dense = in_cloud_ptr->is_dense; + out_cloud_msg_ptr->data.resize(in_cloud_ptr->data.size()); +} + +void RayGroundFilterComponent::ExtractPointsIndices( + const PointCloud2::ConstSharedPtr in_cloud_ptr, const pcl::PointIndices & in_indices, + PointCloud2::SharedPtr ground_cloud_msg_ptr, PointCloud2::SharedPtr no_ground_cloud_msg_ptr) +{ + initializePointCloud2(in_cloud_ptr, ground_cloud_msg_ptr); + initializePointCloud2(in_cloud_ptr, no_ground_cloud_msg_ptr); + int point_step = in_cloud_ptr->point_step; + size_t ground_count = 0; + size_t no_ground_count = 0; + std::vector is_ground_idx(in_cloud_ptr->width, false); + for (const auto & idx : in_indices.indices) { + if (std::size_t(idx) >= is_ground_idx.size()) { + continue; + } + is_ground_idx[idx] = true; + } + for (size_t i = 0; i < is_ground_idx.size(); ++i) { + if (is_ground_idx[i]) { + std::memcpy( + &ground_cloud_msg_ptr->data[ground_count * point_step], &in_cloud_ptr->data[i * point_step], + point_step); + ground_count++; + } else { + std::memcpy( + &no_ground_cloud_msg_ptr->data[no_ground_count * point_step], + &in_cloud_ptr->data[i * point_step], point_step); + no_ground_count++; + } + } + ground_cloud_msg_ptr->data.resize(ground_count * point_step); + no_ground_cloud_msg_ptr->data.resize(no_ground_count * point_step); + ground_cloud_msg_ptr->width = ground_count; + no_ground_cloud_msg_ptr->width = no_ground_count; + ground_cloud_msg_ptr->row_step = ground_count * point_step; + no_ground_cloud_msg_ptr->row_step = no_ground_count * point_step; +} + +void RayGroundFilterComponent::filter( + const PointCloud2::ConstSharedPtr & input, [[maybe_unused]] const IndicesPtr & indices, + PointCloud2 & output) +{ + std::scoped_lock lock(mutex_); + + pcl::PointCloud::Ptr current_sensor_cloud_ptr(new pcl::PointCloud); + pcl::fromROSMsg(*input, *current_sensor_cloud_ptr); + + PointCloudXYZRTColor organized_points; + std::vector radial_division_indices; + std::vector radial_ordered_clouds; + + radial_dividers_num_ = ceil(360 / radial_divider_angle_); + + ConvertXYZIToRTZColor( + current_sensor_cloud_ptr, organized_points, radial_division_indices, radial_ordered_clouds); + + pcl::PointIndices ground_indices, no_ground_indices; + + ClassifyPointCloud(radial_ordered_clouds, ground_indices, no_ground_indices); + + pcl::PointCloud::Ptr ground_cloud_ptr(new pcl::PointCloud); + pcl::PointCloud::Ptr no_ground_cloud_ptr(new pcl::PointCloud); + pcl::PointCloud::Ptr radials_cloud_ptr(new pcl::PointCloud); + + sensor_msgs::msg::PointCloud2::SharedPtr no_ground_cloud_msg_ptr( + new sensor_msgs::msg::PointCloud2); + sensor_msgs::msg::PointCloud2::SharedPtr ground_cloud_msg_ptr(new sensor_msgs::msg::PointCloud2); + + ExtractPointsIndices(input, ground_indices, ground_cloud_msg_ptr, no_ground_cloud_msg_ptr); + output = *no_ground_cloud_msg_ptr; +} + +rcl_interfaces::msg::SetParametersResult RayGroundFilterComponent::paramCallback( + const std::vector & p) +{ + std::scoped_lock lock(mutex_); + + if (get_param(p, "min_x", min_x_)) { + RCLCPP_DEBUG(get_logger(), "Setting min_x to: %f.", min_x_); + } + if (get_param(p, "max_x", max_x_)) { + RCLCPP_DEBUG(get_logger(), "Setting max_x to: %f.", max_x_); + } + if (get_param(p, "min_y", min_y_)) { + RCLCPP_DEBUG(get_logger(), "Setting min_y to: %f.", min_y_); + } + if (get_param(p, "max_y", max_y_)) { + RCLCPP_DEBUG(get_logger(), "Setting max_y to: %f.", max_y_); + } + + setVehicleFootprint(min_x_, max_x_, min_y_, max_y_); + + if (get_param(p, "general_max_slope", general_max_slope_)) { + RCLCPP_DEBUG(get_logger(), "Setting general_max_slope to: %f.", general_max_slope_); + } + if (get_param(p, "local_max_slope", local_max_slope_)) { + RCLCPP_DEBUG(get_logger(), "Setting local_max_slope to: %f.", local_max_slope_); + } + if (get_param(p, "initial_max_slope", initial_max_slope_)) { + RCLCPP_DEBUG(get_logger(), "Setting initial_max_slope to: %f.", initial_max_slope_); + } + if (get_param(p, "radial_divider_angle", radial_divider_angle_)) { + RCLCPP_DEBUG(get_logger(), "Setting radial_divider_angle to: %f.", radial_divider_angle_); + } + if (get_param(p, "concentric_divider_distance", concentric_divider_distance_)) { + RCLCPP_DEBUG( + get_logger(), "Setting concentric_divider_distance to: %f.", concentric_divider_distance_); + } + if (get_param(p, "min_height_threshold", min_height_threshold_)) { + RCLCPP_DEBUG(get_logger(), "Setting min_height_threshold_ to: %f.", min_height_threshold_); + } + if (get_param(p, "reclass_distance_threshold", reclass_distance_threshold_)) { + RCLCPP_DEBUG( + get_logger(), "Setting reclass_distance_threshold to: %f.", reclass_distance_threshold_); + } + if (get_param(p, "use_vehicle_footprint", use_vehicle_footprint_)) { + RCLCPP_DEBUG(get_logger(), "Setting use_vehicle_footprint to: %d.", use_vehicle_footprint_); + } + + rcl_interfaces::msg::SetParametersResult result; + result.successful = true; + result.reason = "success"; + + return result; +} + +} // namespace autoware::ground_segmentation + +#include +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::ground_segmentation::RayGroundFilterComponent) diff --git a/perception/ground_segmentation/src/ray_ground_filter/node.hpp b/perception/ground_segmentation/src/ray_ground_filter/node.hpp new file mode 100644 index 0000000000000..9a2a2d5081a93 --- /dev/null +++ b/perception/ground_segmentation/src/ray_ground_filter/node.hpp @@ -0,0 +1,225 @@ +// Copyright 2020 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// Copyright 2020 Tier IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +/* + * Copyright 2017-2019 Autoware Foundation. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + ******************** + * v1.0: amc-nu (abrahammonrroy@yahoo.com) + */ + +#ifndef RAY_GROUND_FILTER__NODE_HPP_ +#define RAY_GROUND_FILTER__NODE_HPP_ + +#include + +#include +#include +#include +#include + +#ifdef ROS_DISTRO_GALACTIC +#include +#else +#include +#endif + +<<<<<<<< + HEAD : perception / ground_segmentation / src / ray_ground_filter / node.hpp +#include "gencolors.hpp" +#include "pointcloud_preprocessor/filter.hpp" + == == == == +#include "autoware/pointcloud_preprocessor/filter.hpp" +#include "gencolors.hpp" + >>>>>>>> original / + main : perception / autoware_ground_segmentation / src / ray_ground_filter / + node.hpp + +#include +#include + +#include +#include +#include +#include + +#include + +#include +#include +#include + + namespace bg = boost::geometry; +using Point = bg::model::d2::point_xy; +using Polygon = bg::model::polygon; + +namespace autoware::ground_segmentation +{ +class RayGroundFilterComponent : public autoware::pointcloud_preprocessor::Filter +{ + typedef pcl::PointXYZ PointType_; + + struct PointXYZRTColor + { + pcl::PointXYZ point; + + size_t ring; // ring number if available + + float radius; // cylindrical coords on XY Plane + float theta; // angle deg on XY plane + + size_t radial_div; // index of the radial division to which this point belongs to + + size_t red; // Red component [0-255] + size_t green; // Green Component[0-255] + size_t blue; // Blue component [0-255] + + size_t original_index; // index of this point in the source pointcloud + }; + typedef std::vector PointCloudXYZRTColor; + +protected: + void filter( + const PointCloud2ConstPtr & input, const IndicesPtr & indices, PointCloud2 & output) override; + +private: + double general_max_slope_; // degrees + double local_max_slope_; // degrees + double initial_max_slope_; // degrees + double radial_divider_angle_; // distance in rads between dividers + double concentric_divider_distance_; // distance in meters between concentric divisions + double // minimum height threshold regardless the slope + min_height_threshold_; // useful for close points + double + reclass_distance_threshold_; // distance between points at which re classification will occur + + size_t radial_dividers_num_; + + size_t grid_width_; + size_t grid_height_; + double grid_precision_; + cv::Mat previous_occupancy_mat_; + cv::Mat accumulated_occupancy_mat_; + + double min_x_; + double max_x_; + double min_y_; + double max_y_; + + Polygon vehicle_footprint_; + bool use_vehicle_footprint_; + + std::vector colors_; + const size_t color_num_ = 10; // different number of color to generate + pcl::PointCloud::Ptr previous_cloud_ptr_; // holds the previous groundless result of + // ground classification + + /*! + * Output transformed PointCloud from in_cloud_ptr->header.frame_id to in_target_frame + * @param[in] in_target_frame Coordinate system to perform transform + * @param[in] in_cloud_ptr PointCloud to perform transform + * @param[out] out_cloud_ptr Resulting transformed PointCloud + * @retval true transform succeeded + * @retval false transform failed + */ + + /*! + * + * @param[in] in_cloud Input Point Cloud to be organized in radial segments + * @param[out] out_organized_points Custom Point Cloud filled with XYZRTZColor data + * @param[out] out_radial_divided_indices Indices of the points in the original cloud for each + * radial segment + * @param[out] out_radial_ordered_clouds Vector of Points Clouds, each element will contain the + * points ordered + */ + void ConvertXYZIToRTZColor( + const pcl::PointCloud::Ptr in_cloud, PointCloudXYZRTColor & out_organized_points, + std::vector & out_radial_divided_indices, + std::vector & out_radial_ordered_clouds); + + /*! + * Classifies Points in the PointCloud as Ground and Not Ground + * @param in_radial_ordered_clouds Vector of an Ordered PointsCloud ordered by radial distance + * from the origin + * @param out_ground_indices Returns the indices of the points classified as ground in the + * original PointCloud + * @param out_no_ground_indices Returns the indices of the points classified as not ground in the + * original PointCloud + */ + void ClassifyPointCloud( + std::vector & in_radial_ordered_clouds, + pcl::PointIndices & out_ground_indices, pcl::PointIndices & out_no_ground_indices); + + /*! + * Returns the resulting complementary PointCloud, one with the points kept and the other removed + * as indicated in the indices + * @param in_cloud_ptr Input PointCloud to which the extraction will be performed + * @param in_indices Indices of the points to be both removed and kept + * @param out_only_indices_cloud_ptr Resulting PointCloud with the indices kept + * @param out_removed_indices_cloud_ptr Resulting PointCloud with the indices removed + */ + void ExtractPointsIndices(const PointCloud2::ConstSharedPtr in_cloud_ptr, + const pcl::PointIndices & in_indices, < < < < < < < < HEAD + : perception / ground_segmentation / src / ray_ground_filter / + node.hpp PointCloud2::SharedPtr out_only_indices_cloud_ptr, + PointCloud2::SharedPtr out_removed_indices_cloud_ptr); +======== + PointCloud2::SharedPtr ground_cloud_msg_ptr, PointCloud2::SharedPtr no_ground_cloud_msg_ptr); +>>>>>>>> original / main : perception / autoware_ground_segmentation / src / ray_ground_filter / + node.hpp + + boost::optional + calcPointVehicleIntersection(const Point & point); + +void setVehicleFootprint( + const double min_x, const double max_x, const double min_y, const double max_y); +void initializePointCloud2( + const PointCloud2::ConstSharedPtr & in_cloud_ptr, + const PointCloud2::SharedPtr & out_cloud_msg_ptr); +/** \brief Parameter service callback result : needed to be hold */ +OnSetParametersCallbackHandle::SharedPtr set_param_res_; + +/** \brief Parameter service callback */ +rcl_interfaces::msg::SetParametersResult paramCallback(const std::vector & p); + +public: +EIGEN_MAKE_ALIGNED_OPERATOR_NEW +explicit RayGroundFilterComponent(const rclcpp::NodeOptions & options); +}; +} // namespace autoware::ground_segmentation + +#endif // RAY_GROUND_FILTER__NODE_HPP_ diff --git a/perception/ground_segmentation/src/scan_ground_filter/node.cpp b/perception/ground_segmentation/src/scan_ground_filter/node.cpp new file mode 100644 index 0000000000000..52fd0e71ca11d --- /dev/null +++ b/perception/ground_segmentation/src/scan_ground_filter/node.cpp @@ -0,0 +1,726 @@ +// Copyright 2021 Tier IV, Inc. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "node.hpp" + +#include "autoware/universe_utils/geometry/geometry.hpp" +#include "autoware/universe_utils/math/normalization.hpp" +#include "autoware/universe_utils/math/unit_conversion.hpp" +#include "autoware_vehicle_info_utils/vehicle_info_utils.hpp" + +#include +#include +#include + +namespace autoware::ground_segmentation +{ +using autoware::pointcloud_preprocessor::get_param; +using autoware::universe_utils::calcDistance3d; +using autoware::universe_utils::deg2rad; +using autoware::universe_utils::normalizeDegree; +using autoware::universe_utils::normalizeRadian; +using autoware::vehicle_info_utils::VehicleInfoUtils; + +ScanGroundFilterComponent::ScanGroundFilterComponent(const rclcpp::NodeOptions & options)<<<<<<<< + HEAD : perception / ground_segmentation / src / scan_ground_filter / + node.cpp : pointcloud_preprocessor::Filter("ScanGroundFilter", options) == + == == == : autoware::pointcloud_preprocessor::Filter("ScanGroundFilter", options)>>>>>>>> original + / main : perception / autoware_ground_segmentation / src / scan_ground_filter / node.cpp +{ + // set initial parameters + { + low_priority_region_x_ = declare_parameter("low_priority_region_x"); + detection_range_z_max_ = declare_parameter("detection_range_z_max"); + center_pcl_shift_ = declare_parameter("center_pcl_shift"); + non_ground_height_threshold_ = declare_parameter("non_ground_height_threshold"); + grid_mode_switch_radius_ = declare_parameter("grid_mode_switch_radius"); + + grid_size_m_ = declare_parameter("grid_size_m"); + gnd_grid_buffer_size_ = declare_parameter("gnd_grid_buffer_size"); + elevation_grid_mode_ = declare_parameter("elevation_grid_mode"); + global_slope_max_angle_rad_ = deg2rad(declare_parameter("global_slope_max_angle_deg")); + local_slope_max_angle_rad_ = deg2rad(declare_parameter("local_slope_max_angle_deg")); + global_slope_max_ratio_ = std::tan(global_slope_max_angle_rad_); + local_slope_max_ratio_ = std::tan(local_slope_max_angle_rad_); + radial_divider_angle_rad_ = deg2rad(declare_parameter("radial_divider_angle_deg")); + split_points_distance_tolerance_ = declare_parameter("split_points_distance_tolerance"); + split_points_distance_tolerance_square_ = + split_points_distance_tolerance_ * split_points_distance_tolerance_; + split_height_distance_ = declare_parameter("split_height_distance"); + use_virtual_ground_point_ = declare_parameter("use_virtual_ground_point"); + use_recheck_ground_cluster_ = declare_parameter("use_recheck_ground_cluster"); + use_lowest_point_ = declare_parameter("use_lowest_point"); + radial_dividers_num_ = std::ceil(2.0 * M_PI / radial_divider_angle_rad_); + vehicle_info_ = VehicleInfoUtils(*this).getVehicleInfo(); + + grid_mode_switch_grid_id_ = + grid_mode_switch_radius_ / grid_size_m_; // changing the mode of grid division + virtual_lidar_z_ = vehicle_info_.vehicle_height_m; + grid_mode_switch_angle_rad_ = std::atan2(grid_mode_switch_radius_, virtual_lidar_z_); + + grid_size_rad_ = + normalizeRadian(std::atan2(grid_mode_switch_radius_ + grid_size_m_, virtual_lidar_z_)) - + normalizeRadian(std::atan2(grid_mode_switch_radius_, virtual_lidar_z_)); + tan_grid_size_rad_ = std::tan(grid_size_rad_); + offset_initialized_ = false; + } + + using std::placeholders::_1; + set_param_res_ = this->add_on_set_parameters_callback( + std::bind(&ScanGroundFilterComponent::onParameter, this, _1)); + + // initialize debug tool + { + using autoware::universe_utils::DebugPublisher; + using autoware::universe_utils::StopWatch; + stop_watch_ptr_ = std::make_unique>(); + debug_publisher_ptr_ = std::make_unique(this, "scan_ground_filter"); + stop_watch_ptr_->tic("cyclic_time"); + stop_watch_ptr_->tic("processing_time"); + } +} + +inline void ScanGroundFilterComponent::set_field_offsets(const PointCloud2ConstPtr & input) +{ + x_offset_ = input->fields[pcl::getFieldIndex(*input, "x")].offset; + y_offset_ = input->fields[pcl::getFieldIndex(*input, "y")].offset; + z_offset_ = input->fields[pcl::getFieldIndex(*input, "z")].offset; + int intensity_index = pcl::getFieldIndex(*input, "intensity"); + if (intensity_index != -1) { + intensity_offset_ = input->fields[intensity_index].offset; + intensity_type_ = input->fields[intensity_index].datatype; + } else { + intensity_offset_ = -1; + } + offset_initialized_ = true; +} + +inline void ScanGroundFilterComponent::get_point_from_global_offset( + const PointCloud2ConstPtr & input, pcl::PointXYZ & point, size_t global_offset) +{ + point.x = *reinterpret_cast(&input->data[global_offset + x_offset_]); + point.y = *reinterpret_cast(&input->data[global_offset + y_offset_]); + point.z = *reinterpret_cast(&input->data[global_offset + z_offset_]); +} + +void ScanGroundFilterComponent::convertPointcloudGridScan( + const PointCloud2ConstPtr & in_cloud, std::vector & out_radial_ordered_points) +{ + out_radial_ordered_points.resize(radial_dividers_num_); + PointData current_point; + + const auto inv_radial_divider_angle_rad = 1.0f / radial_divider_angle_rad_; + const auto inv_grid_size_rad = 1.0f / grid_size_rad_; + const auto inv_grid_size_m = 1.0f / grid_size_m_; + + const auto grid_id_offset = + grid_mode_switch_grid_id_ - grid_mode_switch_angle_rad_ * inv_grid_size_rad; + const auto x_shift = vehicle_info_.wheel_base_m / 2.0f + center_pcl_shift_; + + const size_t in_cloud_data_size = in_cloud->data.size(); + const size_t in_cloud_point_step = in_cloud->point_step; + + size_t point_index = 0; + pcl::PointXYZ input_point; + for (size_t global_offset = 0; global_offset + in_cloud_point_step <= in_cloud_data_size; + global_offset += in_cloud_point_step) { + get_point_from_global_offset(in_cloud, input_point, global_offset); + + auto x{input_point.x - x_shift}; // base on front wheel center + auto radius{static_cast(std::hypot(x, input_point.y))}; + auto theta{normalizeRadian(std::atan2(x, input_point.y), 0.0)}; + + // divide by vertical angle + auto radial_div{static_cast(std::floor(theta * inv_radial_divider_angle_rad))}; + uint16_t grid_id = 0; + if (radius <= grid_mode_switch_radius_) { + grid_id = static_cast(radius * inv_grid_size_m); + } else { + auto gamma{normalizeRadian(std::atan2(radius, virtual_lidar_z_), 0.0f)}; + grid_id = grid_id_offset + gamma * inv_grid_size_rad; + } + current_point.grid_id = grid_id; + current_point.radius = radius; + current_point.point_state = PointLabel::INIT; + current_point.orig_index = point_index; + + // radial divisions + out_radial_ordered_points[radial_div].emplace_back(current_point); + ++point_index; + } + + // sort by distance + for (size_t i = 0; i < radial_dividers_num_; ++i) { + std::sort( + out_radial_ordered_points[i].begin(), out_radial_ordered_points[i].end(), + [](const PointData & a, const PointData & b) { return a.radius < b.radius; }); + } +} + +void ScanGroundFilterComponent::convertPointcloud( + const PointCloud2ConstPtr & in_cloud, std::vector & out_radial_ordered_points) +{ + out_radial_ordered_points.resize(radial_dividers_num_); + PointData current_point; + + const auto inv_radial_divider_angle_rad = 1.0f / radial_divider_angle_rad_; + + const size_t in_cloud_data_size = in_cloud->data.size(); + const size_t in_cloud_point_step = in_cloud->point_step; + + size_t point_index = 0; + pcl::PointXYZ input_point; + for (size_t global_offset = 0; global_offset + in_cloud_point_step <= in_cloud_data_size; + global_offset += in_cloud_point_step) { + // Point + get_point_from_global_offset(in_cloud, input_point, global_offset); + + auto radius{static_cast(std::hypot(input_point.x, input_point.y))}; + auto theta{normalizeRadian(std::atan2(input_point.x, input_point.y), 0.0)}; + auto radial_div{static_cast(std::floor(theta * inv_radial_divider_angle_rad))}; + + current_point.radius = radius; + current_point.point_state = PointLabel::INIT; + current_point.orig_index = point_index; + + // radial divisions + out_radial_ordered_points[radial_div].emplace_back(current_point); + ++point_index; + } + // sort by distance + for (size_t i = 0; i < radial_dividers_num_; ++i) { + std::sort( + out_radial_ordered_points[i].begin(), out_radial_ordered_points[i].end(), + [](const PointData & a, const PointData & b) { return a.radius < b.radius; }); + } +} + +void ScanGroundFilterComponent::calcVirtualGroundOrigin(pcl::PointXYZ & point) +{ + point.x = vehicle_info_.wheel_base_m; + point.y = 0; + point.z = 0; +} + +inline float ScanGroundFilterComponent::calcGridSize(const PointData & p) +{ + float grid_size = grid_size_m_; + uint16_t back_steps_num = 1; + + if ( + p.radius > grid_mode_switch_radius_ && p.grid_id > grid_mode_switch_grid_id_ + back_steps_num) { + // equivalent to grid_size = (std::tan(gamma) - std::tan(gamma - grid_size_rad_)) * + // virtual_lidar_z_ when gamma = normalizeRadian(std::atan2(radius, virtual_lidar_z_), 0.0f) + grid_size = p.radius - (p.radius - tan_grid_size_rad_ * virtual_lidar_z_) / + (1 + p.radius * tan_grid_size_rad_ / virtual_lidar_z_); + } + return grid_size; +} + +void ScanGroundFilterComponent::initializeFirstGndGrids( + const float h, const float r, const uint16_t id, std::vector & gnd_grids) +{ + GridCenter curr_gnd_grid; + for (int ind_grid = id - 1 - gnd_grid_buffer_size_; ind_grid < id - 1; ++ind_grid) { + float ind_gnd_z = ind_grid - id + 1 + gnd_grid_buffer_size_; + ind_gnd_z *= h / static_cast(gnd_grid_buffer_size_); + + float ind_gnd_radius = ind_grid - id + 1 + gnd_grid_buffer_size_; + ind_gnd_radius *= r / static_cast(gnd_grid_buffer_size_); + + curr_gnd_grid.radius = ind_gnd_radius; + curr_gnd_grid.avg_height = ind_gnd_z; + curr_gnd_grid.max_height = ind_gnd_z; + curr_gnd_grid.grid_id = ind_grid; + gnd_grids.push_back(curr_gnd_grid); + } +} + +void ScanGroundFilterComponent::checkContinuousGndGrid( + PointData & p, const pcl::PointXYZ & p_orig_point, const std::vector & gnd_grids_list) +{ + float next_gnd_z = 0.0f; + float curr_gnd_slope_ratio = 0.0f; + float gnd_buff_z_mean = 0.0f; + float gnd_buff_radius = 0.0f; + + for (auto it = gnd_grids_list.end() - gnd_grid_buffer_size_ - 1; it < gnd_grids_list.end() - 1; + ++it) { + gnd_buff_radius += it->radius; + gnd_buff_z_mean += it->avg_height; + } + + gnd_buff_radius /= static_cast(gnd_grid_buffer_size_ - 1); + gnd_buff_z_mean /= static_cast(gnd_grid_buffer_size_ - 1); + + float tmp_delta_mean_z = gnd_grids_list.back().avg_height - gnd_buff_z_mean; + float tmp_delta_radius = gnd_grids_list.back().radius - gnd_buff_radius; + + curr_gnd_slope_ratio = tmp_delta_mean_z / tmp_delta_radius; + curr_gnd_slope_ratio = curr_gnd_slope_ratio < -global_slope_max_ratio_ ? -global_slope_max_ratio_ + : curr_gnd_slope_ratio; + curr_gnd_slope_ratio = + curr_gnd_slope_ratio > global_slope_max_ratio_ ? global_slope_max_ratio_ : curr_gnd_slope_ratio; + + next_gnd_z = curr_gnd_slope_ratio * (p.radius - gnd_buff_radius) + gnd_buff_z_mean; + + float gnd_z_local_thresh = std::tan(DEG2RAD(5.0)) * (p.radius - gnd_grids_list.back().radius); + + tmp_delta_mean_z = p_orig_point.z - (gnd_grids_list.end() - 2)->avg_height; + tmp_delta_radius = p.radius - (gnd_grids_list.end() - 2)->radius; + float local_slope_ratio = tmp_delta_mean_z / tmp_delta_radius; + if ( + abs(p_orig_point.z - next_gnd_z) <= non_ground_height_threshold_ + gnd_z_local_thresh || + abs(local_slope_ratio) <= local_slope_max_ratio_) { + p.point_state = PointLabel::GROUND; + } else if (p_orig_point.z - next_gnd_z > non_ground_height_threshold_ + gnd_z_local_thresh) { + p.point_state = PointLabel::NON_GROUND; + } +} + +void ScanGroundFilterComponent::checkDiscontinuousGndGrid( + PointData & p, const pcl::PointXYZ & p_orig_point, const std::vector & gnd_grids_list) +{ + float tmp_delta_max_z = p_orig_point.z - gnd_grids_list.back().max_height; + float tmp_delta_avg_z = p_orig_point.z - gnd_grids_list.back().avg_height; + float tmp_delta_radius = p.radius - gnd_grids_list.back().radius; + float local_slope_ratio = tmp_delta_avg_z / tmp_delta_radius; + + if ( + abs(local_slope_ratio) < local_slope_max_ratio_ || + abs(tmp_delta_avg_z) < non_ground_height_threshold_ || + abs(tmp_delta_max_z) < non_ground_height_threshold_) { + p.point_state = PointLabel::GROUND; + } else if (local_slope_ratio > global_slope_max_ratio_) { + p.point_state = PointLabel::NON_GROUND; + } +} + +void ScanGroundFilterComponent::checkBreakGndGrid( + PointData & p, const pcl::PointXYZ & p_orig_point, const std::vector & gnd_grids_list) +{ + float tmp_delta_avg_z = p_orig_point.z - gnd_grids_list.back().avg_height; + float tmp_delta_radius = p.radius - gnd_grids_list.back().radius; + float local_slope_ratio = tmp_delta_avg_z / tmp_delta_radius; + if (abs(local_slope_ratio) < global_slope_max_ratio_) { + p.point_state = PointLabel::GROUND; + } else if (local_slope_ratio > global_slope_max_ratio_) { + p.point_state = PointLabel::NON_GROUND; + } +} + +void ScanGroundFilterComponent::recheckGroundCluster( + PointsCentroid & gnd_cluster, const float non_ground_threshold, const bool use_lowest_point, + pcl::PointIndices & non_ground_indices) +{ + float reference_height = + use_lowest_point ? gnd_cluster.getMinHeight() : gnd_cluster.getAverageHeight(); + const pcl::PointIndices & gnd_indices = gnd_cluster.getIndicesRef(); + const std::vector & height_list = gnd_cluster.getHeightListRef(); + for (size_t i = 0; i < height_list.size(); ++i) { + if (height_list.at(i) >= reference_height + non_ground_threshold) { + non_ground_indices.indices.push_back(gnd_indices.indices.at(i)); + } + } +} + +void ScanGroundFilterComponent::classifyPointCloudGridScan( + const PointCloud2ConstPtr & in_cloud, std::vector & in_radial_ordered_clouds, + pcl::PointIndices & out_no_ground_indices) +{ + out_no_ground_indices.indices.clear(); + for (size_t i = 0; i < in_radial_ordered_clouds.size(); ++i) { + PointsCentroid ground_cluster; + ground_cluster.initialize(); + std::vector gnd_grids; + GridCenter curr_gnd_grid; + + // check empty ray + if (in_radial_ordered_clouds[i].size() == 0) { + continue; + } + + // check the first point in ray + auto * p = &in_radial_ordered_clouds[i][0]; + + bool initialized_first_gnd_grid = false; + bool prev_list_init = false; + pcl::PointXYZ p_orig_point, prev_p_orig_point; + for (auto & point : in_radial_ordered_clouds[i]) { + auto * prev_p = p; // for checking the distance to prev point + prev_p_orig_point = p_orig_point; + p = &point; + get_point_from_global_offset(in_cloud, p_orig_point, in_cloud->point_step * p->orig_index); + float global_slope_ratio_p = p_orig_point.z / p->radius; + float non_ground_height_threshold_local = non_ground_height_threshold_; + if (p_orig_point.x < low_priority_region_x_) { + non_ground_height_threshold_local = + non_ground_height_threshold_ * abs(p_orig_point.x / low_priority_region_x_); + } + // classify first grid's point cloud + if ( + !initialized_first_gnd_grid && global_slope_ratio_p >= global_slope_max_ratio_ && + p_orig_point.z > non_ground_height_threshold_local) { + out_no_ground_indices.indices.push_back(p->orig_index); + p->point_state = PointLabel::NON_GROUND; + continue; + } + + if ( + !initialized_first_gnd_grid && abs(global_slope_ratio_p) < global_slope_max_ratio_ && + abs(p_orig_point.z) < non_ground_height_threshold_local) { + ground_cluster.addPoint(p->radius, p_orig_point.z, p->orig_index); + p->point_state = PointLabel::GROUND; + initialized_first_gnd_grid = static_cast(p->grid_id - prev_p->grid_id); + continue; + } + + if (!initialized_first_gnd_grid) { + continue; + } + + // initialize lists of previous gnd grids + if (!prev_list_init) { + float h = ground_cluster.getAverageHeight(); + float r = ground_cluster.getAverageRadius(); + initializeFirstGndGrids(h, r, p->grid_id, gnd_grids); + prev_list_init = true; + } + + // move to new grid + if (p->grid_id > prev_p->grid_id && ground_cluster.getAverageRadius() > 0.0) { + // check if the prev grid have ground point cloud + if (use_recheck_ground_cluster_) { + recheckGroundCluster( + ground_cluster, non_ground_height_threshold_, use_lowest_point_, out_no_ground_indices); + } + curr_gnd_grid.radius = ground_cluster.getAverageRadius(); + curr_gnd_grid.avg_height = ground_cluster.getAverageHeight(); + curr_gnd_grid.max_height = ground_cluster.getMaxHeight(); + curr_gnd_grid.grid_id = prev_p->grid_id; + gnd_grids.push_back(curr_gnd_grid); + ground_cluster.initialize(); + } + // classify + if (p_orig_point.z - gnd_grids.back().avg_height > detection_range_z_max_) { + p->point_state = PointLabel::OUT_OF_RANGE; + continue; + } + float points_xy_distance_square = + (p_orig_point.x - prev_p_orig_point.x) * (p_orig_point.x - prev_p_orig_point.x) + + (p_orig_point.y - prev_p_orig_point.y) * (p_orig_point.y - prev_p_orig_point.y); + if ( + prev_p->point_state == PointLabel::NON_GROUND && + points_xy_distance_square < split_points_distance_tolerance_square_ && + p_orig_point.z > prev_p_orig_point.z) { + p->point_state = PointLabel::NON_GROUND; + out_no_ground_indices.indices.push_back(p->orig_index); + continue; + } + if (global_slope_ratio_p > global_slope_max_ratio_) { + out_no_ground_indices.indices.push_back(p->orig_index); + continue; + } + // gnd grid is continuous, the last gnd grid is close + uint16_t next_gnd_grid_id_thresh = (gnd_grids.end() - gnd_grid_buffer_size_)->grid_id + + gnd_grid_buffer_size_ + gnd_grid_continual_thresh_; + float curr_grid_size = calcGridSize(*p); + if ( + p->grid_id < next_gnd_grid_id_thresh && + p->radius - gnd_grids.back().radius < gnd_grid_continual_thresh_ * curr_grid_size) { + checkContinuousGndGrid(*p, p_orig_point, gnd_grids); + } else if ( + p->radius - gnd_grids.back().radius < gnd_grid_continual_thresh_ * curr_grid_size) { + checkDiscontinuousGndGrid(*p, p_orig_point, gnd_grids); + } else { + checkBreakGndGrid(*p, p_orig_point, gnd_grids); + } + if (p->point_state == PointLabel::NON_GROUND) { + out_no_ground_indices.indices.push_back(p->orig_index); + } else if (p->point_state == PointLabel::GROUND) { + ground_cluster.addPoint(p->radius, p_orig_point.z, p->orig_index); + } + } + } +} + +void ScanGroundFilterComponent::classifyPointCloud( + const PointCloud2ConstPtr & in_cloud, std::vector & in_radial_ordered_clouds, + pcl::PointIndices & out_no_ground_indices) +{ + out_no_ground_indices.indices.clear(); + + const pcl::PointXYZ init_ground_point(0, 0, 0); + pcl::PointXYZ virtual_ground_point(0, 0, 0); + calcVirtualGroundOrigin(virtual_ground_point); + + // point classification algorithm + // sweep through each radial division + for (size_t i = 0; i < in_radial_ordered_clouds.size(); ++i) { + float prev_gnd_radius = 0.0f; + float prev_gnd_slope = 0.0f; + float points_distance = 0.0f; + PointsCentroid ground_cluster, non_ground_cluster; + PointLabel prev_point_label = PointLabel::INIT; + pcl::PointXYZ prev_gnd_point(0, 0, 0), p_orig_point, prev_p_orig_point; + // loop through each point in the radial div + for (size_t j = 0; j < in_radial_ordered_clouds[i].size(); ++j) { + const float local_slope_max_angle = local_slope_max_angle_rad_; + prev_p_orig_point = p_orig_point; + auto * p = &in_radial_ordered_clouds[i][j]; + get_point_from_global_offset(in_cloud, p_orig_point, in_cloud->point_step * p->orig_index); + if (j == 0) { + bool is_front_side = (p_orig_point.x > virtual_ground_point.x); + if (use_virtual_ground_point_ && is_front_side) { + prev_gnd_point = virtual_ground_point; + } else { + prev_gnd_point = init_ground_point; + } + prev_gnd_radius = std::hypot(prev_gnd_point.x, prev_gnd_point.y); + prev_gnd_slope = 0.0f; + ground_cluster.initialize(); + non_ground_cluster.initialize(); + points_distance = calcDistance3d(p_orig_point, prev_gnd_point); + } else { + points_distance = calcDistance3d(p_orig_point, prev_p_orig_point); + } + + float radius_distance_from_gnd = p->radius - prev_gnd_radius; + float height_from_gnd = p_orig_point.z - prev_gnd_point.z; + float height_from_obj = p_orig_point.z - non_ground_cluster.getAverageHeight(); + bool calculate_slope = false; + bool is_point_close_to_prev = + (points_distance < + (p->radius * radial_divider_angle_rad_ + split_points_distance_tolerance_)); + + float global_slope_ratio = p_orig_point.z / p->radius; + // check points which is far enough from previous point + if (global_slope_ratio > global_slope_max_ratio_) { + p->point_state = PointLabel::NON_GROUND; + calculate_slope = false; + } else if ( + (prev_point_label == PointLabel::NON_GROUND) && + (std::abs(height_from_obj) >= split_height_distance_)) { + calculate_slope = true; + } else if (is_point_close_to_prev && std::abs(height_from_gnd) < split_height_distance_) { + // close to the previous point, set point follow label + p->point_state = PointLabel::POINT_FOLLOW; + calculate_slope = false; + } else { + calculate_slope = true; + } + if (is_point_close_to_prev) { + height_from_gnd = p_orig_point.z - ground_cluster.getAverageHeight(); + radius_distance_from_gnd = p->radius - ground_cluster.getAverageRadius(); + } + if (calculate_slope) { + // far from the previous point + auto local_slope = std::atan2(height_from_gnd, radius_distance_from_gnd); + if (local_slope - prev_gnd_slope > local_slope_max_angle) { + // the point is outside of the local slope threshold + p->point_state = PointLabel::NON_GROUND; + } else { + p->point_state = PointLabel::GROUND; + } + } + + if (p->point_state == PointLabel::GROUND) { + ground_cluster.initialize(); + non_ground_cluster.initialize(); + } + if (p->point_state == PointLabel::NON_GROUND) { + out_no_ground_indices.indices.push_back(p->orig_index); + } else if ( // NOLINT + (prev_point_label == PointLabel::NON_GROUND) && + (p->point_state == PointLabel::POINT_FOLLOW)) { + p->point_state = PointLabel::NON_GROUND; + out_no_ground_indices.indices.push_back(p->orig_index); + } else if ( // NOLINT + (prev_point_label == PointLabel::GROUND) && (p->point_state == PointLabel::POINT_FOLLOW)) { + p->point_state = PointLabel::GROUND; + } else { + } + + // update the ground state + prev_point_label = p->point_state; + if (p->point_state == PointLabel::GROUND) { + prev_gnd_radius = p->radius; + prev_gnd_point = pcl::PointXYZ(p_orig_point.x, p_orig_point.y, p_orig_point.z); + ground_cluster.addPoint(p->radius, p_orig_point.z); + prev_gnd_slope = ground_cluster.getAverageSlope(); + } + // update the non ground state + if (p->point_state == PointLabel::NON_GROUND) { + non_ground_cluster.addPoint(p->radius, p_orig_point.z); + } + } + } +} + +void ScanGroundFilterComponent::extractObjectPoints( + const PointCloud2ConstPtr & in_cloud_ptr, const pcl::PointIndices & in_indices, + PointCloud2 & out_object_cloud) +{ + size_t output_data_size = 0; + + for (const auto & i : in_indices.indices) { + std::memcpy( + &out_object_cloud.data[output_data_size], &in_cloud_ptr->data[i * in_cloud_ptr->point_step], + in_cloud_ptr->point_step * sizeof(uint8_t)); + output_data_size += in_cloud_ptr->point_step; + } +} + +void ScanGroundFilterComponent::faster_filter( + const PointCloud2ConstPtr & input, [[maybe_unused]] const IndicesPtr & indices, + PointCloud2 & output, + [[maybe_unused]] const autoware::pointcloud_preprocessor::TransformInfo & transform_info) +{ + std::scoped_lock lock(mutex_); + stop_watch_ptr_->toc("processing_time", true); + + if (!offset_initialized_) { + set_field_offsets(input); + } + std::vector radial_ordered_points; + + pcl::PointIndices no_ground_indices; + + if (elevation_grid_mode_) { + convertPointcloudGridScan(input, radial_ordered_points); + classifyPointCloudGridScan(input, radial_ordered_points, no_ground_indices); + } else { + convertPointcloud(input, radial_ordered_points); + classifyPointCloud(input, radial_ordered_points, no_ground_indices); + } + output.row_step = no_ground_indices.indices.size() * input->point_step; + output.data.resize(output.row_step); + output.width = no_ground_indices.indices.size(); + output.fields = input->fields; + output.is_dense = true; + output.height = input->height; + output.is_bigendian = input->is_bigendian; + output.point_step = input->point_step; + output.header = input->header; + + extractObjectPoints(input, no_ground_indices, output); + if (debug_publisher_ptr_ && stop_watch_ptr_) { + const double cyclic_time_ms = stop_watch_ptr_->toc("cyclic_time", true); + const double processing_time_ms = stop_watch_ptr_->toc("processing_time", true); + debug_publisher_ptr_->publish( + "debug/cyclic_time_ms", cyclic_time_ms); + debug_publisher_ptr_->publish( + "debug/processing_time_ms", processing_time_ms); + } +} + +// TODO(taisa1): Temporary Implementation: Delete this function definition when all the filter +// nodes conform to new API. +void ScanGroundFilterComponent::filter( + const PointCloud2ConstPtr & input, [[maybe_unused]] const IndicesPtr & indices, + PointCloud2 & output) +{ + (void)input; + (void)indices; + (void)output; +} + +rcl_interfaces::msg::SetParametersResult ScanGroundFilterComponent::onParameter( + const std::vector & p) +{ + if (get_param(p, "grid_size_m", grid_size_m_)) { + grid_mode_switch_grid_id_ = grid_mode_switch_radius_ / grid_size_m_; + grid_size_rad_ = + normalizeRadian(std::atan2(grid_mode_switch_radius_ + grid_size_m_, virtual_lidar_z_)) - + normalizeRadian(std::atan2(grid_mode_switch_radius_, virtual_lidar_z_)); + tan_grid_size_rad_ = std::tan(grid_size_rad_); + RCLCPP_DEBUG(get_logger(), "Setting grid_size_m to: %f.", grid_size_m_); + RCLCPP_DEBUG( + get_logger(), "Setting grid_mode_switch_grid_id to: %f.", grid_mode_switch_grid_id_); + RCLCPP_DEBUG(get_logger(), "Setting grid_size_rad to: %f.", grid_size_rad_); + RCLCPP_DEBUG(get_logger(), "Setting tan_grid_size_rad to: %f.", tan_grid_size_rad_); + } + if (get_param(p, "grid_mode_switch_radius", grid_mode_switch_radius_)) { + grid_mode_switch_grid_id_ = grid_mode_switch_radius_ / grid_size_m_; + grid_mode_switch_angle_rad_ = std::atan2(grid_mode_switch_radius_, virtual_lidar_z_); + grid_size_rad_ = + normalizeRadian(std::atan2(grid_mode_switch_radius_ + grid_size_m_, virtual_lidar_z_)) - + normalizeRadian(std::atan2(grid_mode_switch_radius_, virtual_lidar_z_)); + tan_grid_size_rad_ = std::tan(grid_size_rad_); + RCLCPP_DEBUG(get_logger(), "Setting grid_mode_switch_radius to: %f.", grid_mode_switch_radius_); + RCLCPP_DEBUG( + get_logger(), "Setting grid_mode_switch_grid_id to: %f.", grid_mode_switch_grid_id_); + RCLCPP_DEBUG( + get_logger(), "Setting grid_mode_switch_angle_rad to: %f.", grid_mode_switch_angle_rad_); + RCLCPP_DEBUG(get_logger(), "Setting grid_size_rad to: %f.", grid_size_rad_); + RCLCPP_DEBUG(get_logger(), "Setting tan_grid_size_rad to: %f.", tan_grid_size_rad_); + } + double global_slope_max_angle_deg{get_parameter("global_slope_max_angle_deg").as_double()}; + if (get_param(p, "global_slope_max_angle_deg", global_slope_max_angle_deg)) { + global_slope_max_angle_rad_ = deg2rad(global_slope_max_angle_deg); + global_slope_max_ratio_ = std::tan(global_slope_max_angle_rad_); + RCLCPP_DEBUG( + get_logger(), "Setting global_slope_max_angle_rad to: %f.", global_slope_max_angle_rad_); + RCLCPP_DEBUG(get_logger(), "Setting global_slope_max_ratio to: %f.", global_slope_max_ratio_); + } + double local_slope_max_angle_deg{get_parameter("local_slope_max_angle_deg").as_double()}; + if (get_param(p, "local_slope_max_angle_deg", local_slope_max_angle_deg)) { + local_slope_max_angle_rad_ = deg2rad(local_slope_max_angle_deg); + local_slope_max_ratio_ = std::tan(local_slope_max_angle_rad_); + RCLCPP_DEBUG( + get_logger(), "Setting local_slope_max_angle_rad to: %f.", local_slope_max_angle_rad_); + RCLCPP_DEBUG(get_logger(), "Setting local_slope_max_ratio to: %f.", local_slope_max_ratio_); + } + double radial_divider_angle_deg{get_parameter("radial_divider_angle_deg").as_double()}; + if (get_param(p, "radial_divider_angle_deg", radial_divider_angle_deg)) { + radial_divider_angle_rad_ = deg2rad(radial_divider_angle_deg); + radial_dividers_num_ = std::ceil(2.0 * M_PI / radial_divider_angle_rad_); + RCLCPP_DEBUG( + get_logger(), "Setting radial_divider_angle_rad to: %f.", radial_divider_angle_rad_); + RCLCPP_DEBUG(get_logger(), "Setting radial_dividers_num to: %zu.", radial_dividers_num_); + } + if (get_param(p, "split_points_distance_tolerance", split_points_distance_tolerance_)) { + split_points_distance_tolerance_square_ = + split_points_distance_tolerance_ * split_points_distance_tolerance_; + RCLCPP_DEBUG( + get_logger(), "Setting split_points_distance_tolerance to: %f.", + split_points_distance_tolerance_); + RCLCPP_DEBUG( + get_logger(), "Setting split_points_distance_tolerance_square to: %f.", + split_points_distance_tolerance_square_); + } + if (get_param(p, "split_height_distance", split_height_distance_)) { + RCLCPP_DEBUG(get_logger(), "Setting split_height_distance to: %f.", split_height_distance_); + } + if (get_param(p, "use_virtual_ground_point", use_virtual_ground_point_)) { + RCLCPP_DEBUG_STREAM( + get_logger(), + "Setting use_virtual_ground_point to: " << std::boolalpha << use_virtual_ground_point_); + } + if (get_param(p, "use_recheck_ground_cluster", use_recheck_ground_cluster_)) { + RCLCPP_DEBUG_STREAM( + get_logger(), + "Setting use_recheck_ground_cluster to: " << std::boolalpha << use_recheck_ground_cluster_); + } + rcl_interfaces::msg::SetParametersResult result; + result.successful = true; + result.reason = "success"; + + return result; +} + +} // namespace autoware::ground_segmentation + +#include +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::ground_segmentation::ScanGroundFilterComponent) diff --git a/perception/ground_segmentation/src/scan_ground_filter/node.hpp b/perception/ground_segmentation/src/scan_ground_filter/node.hpp new file mode 100644 index 0000000000000..3f6ad785fa519 --- /dev/null +++ b/perception/ground_segmentation/src/scan_ground_filter/node.hpp @@ -0,0 +1,306 @@ +// Copyright 2021 Tier IV, Inc. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef SCAN_GROUND_FILTER__NODE_HPP_ +#define SCAN_GROUND_FILTER__NODE_HPP_ + +<<<<<<<< + HEAD : perception / ground_segmentation / src / scan_ground_filter / node.hpp +#include "autoware_vehicle_info_utils/vehicle_info.hpp" +#include "pointcloud_preprocessor/filter.hpp" +#include "pointcloud_preprocessor/transform_info.hpp" + == == == == +#include "autoware/pointcloud_preprocessor/filter.hpp" +#include "autoware/pointcloud_preprocessor/transform_info.hpp" +#include "autoware_vehicle_info_utils/vehicle_info.hpp" + >>>>>>>> original / + main : perception / autoware_ground_segmentation / src / scan_ground_filter / + node.hpp + +#include + +#include +#include +#include +#include + +#ifdef ROS_DISTRO_GALACTIC +#include +#else +#include +#endif + +#include + +#include +#include +#include + + class ScanGroundFilterTest; + +namespace autoware::ground_segmentation +{ +using autoware::vehicle_info_utils::VehicleInfo; + +class ScanGroundFilterComponent : public autoware::pointcloud_preprocessor::Filter +{ +private: + // classified point label + // (0: not classified, 1: ground, 2: not ground, 3: follow previous point, + // 4: unkown(currently not used), 5: virtual ground) + enum class PointLabel : uint16_t { + INIT = 0, + GROUND, + NON_GROUND, + POINT_FOLLOW, + UNKNOWN, + VIRTUAL_GROUND, + OUT_OF_RANGE + }; + + struct PointData + { + float radius; // cylindrical coords on XY Plane + PointLabel point_state{PointLabel::INIT}; + uint16_t grid_id; // id of grid in vertical + size_t orig_index; // index of this point in the source pointcloud + }; + using PointCloudVector = std::vector; + + struct GridCenter + { + float radius; + float avg_height; + float max_height; + uint16_t grid_id; + }; + + struct PointsCentroid + { + float radius_sum; + float height_sum; + float radius_avg; + float height_avg; + float height_max; + float height_min; + uint32_t point_num; + uint16_t grid_id; + pcl::PointIndices pcl_indices; + std::vector height_list; + + PointsCentroid() + : radius_sum(0.0f), height_sum(0.0f), radius_avg(0.0f), height_avg(0.0f), point_num(0) + { + } + + void initialize() + { + radius_sum = 0.0f; + height_sum = 0.0f; + radius_avg = 0.0f; + height_avg = 0.0f; + height_max = 0.0f; + height_min = 10.0f; + point_num = 0; + grid_id = 0; + pcl_indices.indices.clear(); + height_list.clear(); + } + + void addPoint(const float radius, const float height) + { + radius_sum += radius; + height_sum += height; + ++point_num; + radius_avg = radius_sum / point_num; + height_avg = height_sum / point_num; + height_max = height_max < height ? height : height_max; + height_min = height_min > height ? height : height_min; + } + void addPoint(const float radius, const float height, const uint index) + { + pcl_indices.indices.push_back(index); + height_list.push_back(height); + addPoint(radius, height); + } + + float getAverageSlope() { return std::atan2(height_avg, radius_avg); } + + float getAverageHeight() { return height_avg; } + + float getAverageRadius() { return radius_avg; } + + float getMaxHeight() { return height_max; } + + float getMinHeight() { return height_min; } + + uint16_t getGridId() { return grid_id; } + + pcl::PointIndices getIndices() { return pcl_indices; } + std::vector getHeightList() { return height_list; } + + pcl::PointIndices & getIndicesRef() { return pcl_indices; } + std::vector & getHeightListRef() { return height_list; } + }; + + void filter( + const PointCloud2ConstPtr & input, const IndicesPtr & indices, PointCloud2 & output) override; + + // TODO(taisa1): Temporary Implementation: Remove this interface when all the filter nodes + // conform to new API + virtual void faster_filter( + const PointCloud2ConstPtr & input, const IndicesPtr & indices, PointCloud2 & output, + const autoware::pointcloud_preprocessor::TransformInfo & transform_info); + + tf2_ros::Buffer tf_buffer_{get_clock()}; + tf2_ros::TransformListener tf_listener_{tf_buffer_}; + + int x_offset_; + int y_offset_; + int z_offset_; + int intensity_offset_; + int intensity_type_; + bool offset_initialized_; + + void set_field_offsets(const PointCloud2ConstPtr & input); + + void get_point_from_global_offset( + const PointCloud2ConstPtr & input, pcl::PointXYZ & point, size_t global_offset); + + const uint16_t gnd_grid_continual_thresh_ = 3; + bool elevation_grid_mode_; + float non_ground_height_threshold_; + float grid_size_rad_; + float tan_grid_size_rad_; + float grid_size_m_; + float low_priority_region_x_; + uint16_t gnd_grid_buffer_size_; + float grid_mode_switch_grid_id_; + float grid_mode_switch_angle_rad_; + float virtual_lidar_z_; + float detection_range_z_max_; + float center_pcl_shift_; // virtual center of pcl to center mass + float grid_mode_switch_radius_; // non linear grid size switching distance + double global_slope_max_angle_rad_; // radians + double local_slope_max_angle_rad_; // radians + double global_slope_max_ratio_; + double local_slope_max_ratio_; + double radial_divider_angle_rad_; // distance in rads between dividers + double split_points_distance_tolerance_; // distance in meters between concentric divisions + double split_points_distance_tolerance_square_; + double // minimum height threshold regardless the slope, + split_height_distance_; // useful for close points + bool use_virtual_ground_point_; + bool use_recheck_ground_cluster_; // to enable recheck ground cluster + bool use_lowest_point_; // to select lowest point for reference in recheck ground cluster, + // otherwise select middle point + size_t radial_dividers_num_; + VehicleInfo vehicle_info_; + + /*! + * Output transformed PointCloud from in_cloud_ptr->header.frame_id to in_target_frame + * @param[in] in_target_frame Coordinate system to perform transform + * @param[in] in_cloud_ptr PointCloud to perform transform + * @param[out] out_cloud_ptr Resulting transformed PointCloud + * @retval true transform succeeded + * @retval false transform failed + */ + + /*! + * Convert sensor_msgs::msg::PointCloud2 to sorted PointCloudVector + * @param[in] in_cloud Input Point Cloud to be organized in radial segments + * @param[out] out_radial_ordered_points Vector of Points Clouds, + * each element will contain the points ordered + */ + void convertPointcloud( + const PointCloud2ConstPtr & in_cloud, + std::vector & out_radial_ordered_points); + void convertPointcloudGridScan( + const PointCloud2ConstPtr & in_cloud, + std::vector & out_radial_ordered_points); + /*! + * Output ground center of front wheels as the virtual ground point + * @param[out] point Virtual ground origin point + */ + void calcVirtualGroundOrigin(pcl::PointXYZ & point); + + float calcGridSize(const PointData & p); + + /*! + * Classifies Points in the PointCloud as Ground and Not Ground + * @param in_radial_ordered_clouds Vector of an Ordered PointsCloud + * ordered by radial distance from the origin + * @param out_no_ground_indices Returns the indices of the points + * classified as not ground in the original PointCloud + */ + + void initializeFirstGndGrids( + const float h, const float r, const uint16_t id, std::vector & gnd_grids); + + void checkContinuousGndGrid( + PointData & p, const pcl::PointXYZ & p_orig_point, + const std::vector & gnd_grids_list); + void checkDiscontinuousGndGrid( + PointData & p, const pcl::PointXYZ & p_orig_point, + const std::vector & gnd_grids_list); + void checkBreakGndGrid( + PointData & p, const pcl::PointXYZ & p_orig_point, + const std::vector & gnd_grids_list); + void classifyPointCloud( + const PointCloud2ConstPtr & in_cloud, std::vector & in_radial_ordered_clouds, + pcl::PointIndices & out_no_ground_indices); + void classifyPointCloudGridScan( + const PointCloud2ConstPtr & in_cloud, std::vector & in_radial_ordered_clouds, + pcl::PointIndices & out_no_ground_indices); + /*! + * Re-classifies point of ground cluster based on their height + * @param gnd_cluster Input ground cluster for re-checking + * @param non_ground_threshold Height threshold for ground and non-ground points classification + * @param non_ground_indices Output non-ground PointCloud indices + */ + void recheckGroundCluster( + PointsCentroid & gnd_cluster, const float non_ground_threshold, const bool use_lowest_point, + pcl::PointIndices & non_ground_indices); + /*! + * Returns the resulting complementary PointCloud, one with the points kept + * and the other removed as indicated in the indices + * @param in_cloud_ptr Input PointCloud to which the extraction will be performed + * @param in_indices Indices of the points to be both removed and kept + * @param out_object_cloud Resulting PointCloud with the indices kept + */ + void extractObjectPoints( + const PointCloud2ConstPtr & in_cloud_ptr, const pcl::PointIndices & in_indices, + PointCloud2 & out_object_cloud); + + /** \brief Parameter service callback result : needed to be hold */ + OnSetParametersCallbackHandle::SharedPtr set_param_res_; + + /** \brief Parameter service callback */ + rcl_interfaces::msg::SetParametersResult onParameter(const std::vector & p); + + // debugger + std::unique_ptr> stop_watch_ptr_{ + nullptr}; + std::unique_ptr debug_publisher_ptr_{nullptr}; + +public: + EIGEN_MAKE_ALIGNED_OPERATOR_NEW + explicit ScanGroundFilterComponent(const rclcpp::NodeOptions & options); + + // for test + friend ScanGroundFilterTest; +}; +} // namespace autoware::ground_segmentation + +#endif // SCAN_GROUND_FILTER__NODE_HPP_ diff --git a/perception/ground_segmentation/test/data/test.pcd b/perception/ground_segmentation/test/data/test.pcd new file mode 100644 index 0000000000000..2df8ca09a18f9 --- /dev/null +++ b/perception/ground_segmentation/test/data/test.pcd @@ -0,0 +1,65547 @@ +# .PCD v0.7 - Point Cloud Data file format +VERSION 0.7 +FIELDS x y z intensity +SIZE 4 4 4 4 +TYPE F F F F +COUNT 1 1 1 1 +WIDTH 1024 +HEIGHT 64 +VIEWPOINT 0 0 0 1 0 0 0 +POINTS 65536 +DATA ascii +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 0 128.0 +-0 0 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CURBSTONE: false + CROSSWALK: false + VEGETATION: true + SKY: false + + # the maximum distance of pointcloud to be applied filter + filter_distance_threshold: 60.0 diff --git a/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/debugger.hpp b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/debugger.hpp new file mode 100644 index 0000000000000..ba7f8b6ca3496 --- /dev/null +++ b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/debugger.hpp @@ -0,0 +1,71 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__DEBUGGER_HPP_ +#define AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__DEBUGGER_HPP_ + +#define EIGEN_MPL2_ONLY + +#include +#include +#include +#include + +#include +#include + +#include + +#include +#include +#include + +namespace autoware::image_projection_based_fusion +{ + +using sensor_msgs::msg::RegionOfInterest; + +class Debugger +{ +public: + explicit Debugger( + rclcpp::Node * node_ptr, const std::size_t image_num, const std::size_t image_buffer_size, + std::vector input_camera_topics); + + void publishImage(const std::size_t image_id, const rclcpp::Time & stamp); + + void clear(); + + std::vector image_rois_; + std::vector obstacle_rois_; + std::vector obstacle_points_; + std::vector max_iou_for_image_rois_; + +private: + void imageCallback( + const sensor_msgs::msg::Image::ConstSharedPtr input_image_msg, const std::size_t image_id); + + rclcpp::Node * node_ptr_; + std::shared_ptr image_transport_; + std::vector input_camera_topics_; + std::vector image_subs_; + std::vector image_pubs_; + std::vector> image_buffers_; + + std::size_t image_buffer_size_; +}; + +} // namespace autoware::image_projection_based_fusion + +#endif // AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__DEBUGGER_HPP_ diff --git a/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/fusion_node.hpp b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/fusion_node.hpp new file mode 100644 index 0000000000000..d4e498212ac36 --- /dev/null +++ b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/fusion_node.hpp @@ -0,0 +1,147 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__FUSION_NODE_HPP_ +#define AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__FUSION_NODE_HPP_ + +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +namespace autoware::image_projection_based_fusion +{ +using autoware_perception_msgs::msg::DetectedObject; +using autoware_perception_msgs::msg::DetectedObjects; +using sensor_msgs::msg::CameraInfo; +using sensor_msgs::msg::Image; +using sensor_msgs::msg::PointCloud2; +using tier4_perception_msgs::msg::DetectedObjectsWithFeature; +using tier4_perception_msgs::msg::DetectedObjectWithFeature; +using PointCloud = pcl::PointCloud; +using autoware_perception_msgs::msg::ObjectClassification; + +template +class FusionNode : public rclcpp::Node +{ +public: + /** \brief constructor. */ + explicit FusionNode(const std::string & node_name, const rclcpp::NodeOptions & options); + /** \brief constructor. + * \param queue_size the maximum queue size + */ + explicit FusionNode( + const std::string & node_name, const rclcpp::NodeOptions & options, int queue_size); + +protected: + void cameraInfoCallback( + const sensor_msgs::msg::CameraInfo::ConstSharedPtr input_camera_info_msg, + const std::size_t camera_id); + + virtual void preprocess(TargetMsg3D & output_msg); + + // callback for Msg subscription + virtual void subCallback(const typename TargetMsg3D::ConstSharedPtr input_msg); + + // callback for roi subscription + + virtual void roiCallback( + const typename Msg2D::ConstSharedPtr input_roi_msg, const std::size_t roi_i); + + virtual void fuseOnSingleImage( + const TargetMsg3D & input_msg, const std::size_t image_id, const Msg2D & input_roi_msg, + const sensor_msgs::msg::CameraInfo & camera_info, TargetMsg3D & output_msg) = 0; + + // set args if you need + virtual void postprocess(TargetMsg3D & output_msg); + + virtual void publish(const TargetMsg3D & output_msg); + + void timer_callback(); + void setPeriod(const int64_t new_period); + + std::size_t rois_number_{1}; + tf2_ros::Buffer tf_buffer_; + tf2_ros::TransformListener tf_listener_; + + // camera_info + std::map camera_info_map_; + std::vector::SharedPtr> camera_info_subs_; + + rclcpp::TimerBase::SharedPtr timer_; + double timeout_ms_{}; + double match_threshold_ms_{}; + std::vector input_rois_topics_; + std::vector input_camera_info_topics_; + std::vector input_camera_topics_; + + /** \brief A vector of subscriber. */ + typename rclcpp::Subscription::SharedPtr sub_; + std::vector::SharedPtr> rois_subs_; + + // offsets between cameras and the lidars + std::vector input_offset_ms_; + + // cache for fusion + std::vector is_fused_; + std::pair + cached_msg_; // first element is the timestamp in nanoseconds, second element is the message + std::vector> cached_roi_msgs_; + std::mutex mutex_cached_msgs_; + + // output publisher + typename rclcpp::Publisher::SharedPtr pub_ptr_; + + // debugger + std::shared_ptr debugger_; + virtual bool out_of_scope(const ObjType & obj) = 0; + float filter_scope_min_x_; + float filter_scope_max_x_; + float filter_scope_min_y_; + float filter_scope_max_y_; + float filter_scope_min_z_; + float filter_scope_max_z_; + + /** \brief processing time publisher. **/ + std::unique_ptr> stop_watch_ptr_; + std::unique_ptr debug_publisher_; +}; + +} // namespace autoware::image_projection_based_fusion + +#endif // AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__FUSION_NODE_HPP_ diff --git a/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/pointpainting_fusion/node.hpp b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/pointpainting_fusion/node.hpp new file mode 100644 index 0000000000000..5aa6bcf47def6 --- /dev/null +++ b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/pointpainting_fusion/node.hpp @@ -0,0 +1,96 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__NODE_HPP_ +#define AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__NODE_HPP_ + +#include "autoware/image_projection_based_fusion/fusion_node.hpp" +#include "autoware/image_projection_based_fusion/pointpainting_fusion/pointpainting_trt.hpp" +<<<<<<<< + HEAD : perception / image_projection_based_fusion / include / autoware / + image_projection_based_fusion / pointpainting_fusion / node.hpp +#include "lidar_centerpoint/postprocess/non_maximum_suppression.hpp" + +#include +#include +#include +#include + == == == == +#include "autoware/lidar_centerpoint/postprocess/non_maximum_suppression.hpp" + +#include +#include +#include +#include + >>>>>>>> original / + main : perception / autoware_image_projection_based_fusion / include / autoware / + image_projection_based_fusion / pointpainting_fusion / + node.hpp + +#include +#include +#include +#include + + namespace autoware::image_projection_based_fusion +{ + using Label = autoware_perception_msgs::msg::ObjectClassification; + + inline bool isInsideBbox( + float proj_x, float proj_y, sensor_msgs::msg::RegionOfInterest roi, float zc) + { + // z_c is scaling to normalize projection point + return proj_x >= roi.x_offset * zc && proj_x <= (roi.x_offset + roi.width) * zc && + proj_y >= roi.y_offset * zc && proj_y <= (roi.y_offset + roi.height) * zc; + } + + class PointPaintingFusionNode + : public FusionNode + { + public: + explicit PointPaintingFusionNode(const rclcpp::NodeOptions & options); + + protected: + void preprocess(sensor_msgs::msg::PointCloud2 & pointcloud_msg) override; + + void fuseOnSingleImage( + const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const std::size_t image_id, + const DetectedObjectsWithFeature & input_roi_msg, + const sensor_msgs::msg::CameraInfo & camera_info, + sensor_msgs::msg::PointCloud2 & painted_pointcloud_msg) override; + + void postprocess(sensor_msgs::msg::PointCloud2 & painted_pointcloud_msg) override; + + rclcpp::Publisher::SharedPtr obj_pub_ptr_; + + std::vector tan_h_; // horizontal field of view + + int omp_num_threads_{1}; + float score_threshold_{0.0}; + std::vector class_names_; + std::map class_index_; + std::map> isClassTable_; + std::vector pointcloud_range; + bool has_variance_{false}; + bool has_twist_{false}; + + autoware::lidar_centerpoint::NonMaximumSuppression iou_bev_nms_; + autoware::lidar_centerpoint::DetectionClassRemapper detection_class_remapper_; + + std::unique_ptr detector_ptr_{nullptr}; + + bool out_of_scope(const DetectedObjects & obj); + }; +} // namespace autoware::image_projection_based_fusion +#endif // AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__NODE_HPP_ diff --git a/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/pointpainting_fusion/pointcloud_densification.hpp b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/pointpainting_fusion/pointcloud_densification.hpp new file mode 100644 index 0000000000000..35cfe48d15343 --- /dev/null +++ b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/pointpainting_fusion/pointcloud_densification.hpp @@ -0,0 +1,73 @@ +// Copyright 2024 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__POINTCLOUD_DENSIFICATION_HPP_ +#define AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__POINTCLOUD_DENSIFICATION_HPP_ + +#include +#include +#ifdef ROS_DISTRO_GALACTIC +#include +#else +#include +#endif + +#include + +#include +#include +#include + +namespace autoware::image_projection_based_fusion +{ +struct PointCloudWithTransform +{ + sensor_msgs::msg::PointCloud2 pointcloud_msg; + Eigen::Affine3f affine_past2world; +}; + +class PointCloudDensification +{ +public: + explicit PointCloudDensification(const autoware::lidar_centerpoint::DensificationParam & param); + + bool enqueuePointCloud( + const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const tf2_ros::Buffer & tf_buffer); + + double getCurrentTimestamp() const { return current_timestamp_; } + Eigen::Affine3f getAffineWorldToCurrent() const { return affine_world2current_; } + std::list::iterator getPointCloudCacheIter() + { + return pointcloud_cache_.begin(); + } + bool isCacheEnd(std::list::iterator iter) + { + return iter == pointcloud_cache_.end(); + } + unsigned int pointcloud_cache_size() const { return param_.pointcloud_cache_size(); } + +private: + void enqueue(const sensor_msgs::msg::PointCloud2 & msg, const Eigen::Affine3f & affine); + void dequeue(); + + autoware::lidar_centerpoint::DensificationParam param_; + double current_timestamp_{0.0}; + Eigen::Affine3f affine_world2current_; + std::list pointcloud_cache_; +}; + +} // namespace autoware::image_projection_based_fusion + +// NOLINTNEXTLINE(whitespace/line_length) +#endif // AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__POINTCLOUD_DENSIFICATION_HPP_ diff --git a/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/pointpainting_fusion/pointpainting_trt.hpp b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/pointpainting_fusion/pointpainting_trt.hpp new file mode 100644 index 0000000000000..daf925c2ff9f3 --- /dev/null +++ b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/pointpainting_fusion/pointpainting_trt.hpp @@ -0,0 +1,45 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__POINTPAINTING_TRT_HPP_ +#define AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__POINTPAINTING_TRT_HPP_ + +#include +#include + +#include +#include + +namespace autoware::image_projection_based_fusion +{ +class PointPaintingTRT : public centerpoint::CenterPointTRT +{ +public: + using centerpoint::CenterPointTRT::CenterPointTRT; + + explicit PointPaintingTRT( + const centerpoint::NetworkParam & encoder_param, const centerpoint::NetworkParam & head_param, + const centerpoint::DensificationParam & densification_param, + const centerpoint::CenterPointConfig & config); + +protected: + bool preprocess( + const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, + const tf2_ros::Buffer & tf_buffer) override; + + std::unique_ptr vg_ptr_pp_{nullptr}; +}; +} // namespace autoware::image_projection_based_fusion + +#endif // AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__POINTPAINTING_TRT_HPP_ diff --git a/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/pointpainting_fusion/preprocess_kernel.hpp b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/pointpainting_fusion/preprocess_kernel.hpp new file mode 100644 index 0000000000000..70a9fbb4a2244 --- /dev/null +++ b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/pointpainting_fusion/preprocess_kernel.hpp @@ -0,0 +1,43 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__PREPROCESS_KERNEL_HPP_ +#define AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__PREPROCESS_KERNEL_HPP_ + +#include +#include + +namespace autoware::image_projection_based_fusion +{ +cudaError_t generateVoxels_random_launch( + const float * points, size_t points_size, float min_x_range, float max_x_range, float min_y_range, + float max_y_range, float min_z_range, float max_z_range, float pillar_x_size, float pillar_y_size, + float pillar_z_size, int grid_y_size, int grid_x_size, unsigned int * mask, float * voxels, + cudaStream_t stream); + +cudaError_t generateBaseFeatures_launch( + unsigned int * mask, float * voxels, int grid_y_size, int grid_x_size, int max_voxel_size, + unsigned int * pillar_num, float * voxel_features, float * voxel_num, int * voxel_idxs, + cudaStream_t stream); + +cudaError_t generateFeatures_launch( + const float * voxel_features, const float * voxel_num_points, const int * coords, + const unsigned int * num_voxels, const std::size_t max_voxel_size, const float voxel_size_x, + const float voxel_size_y, const float voxel_size_z, const float range_min_x, + const float range_min_y, const float range_min_z, float * features, + const std::size_t encoder_in_feature_size, cudaStream_t stream); + +} // namespace autoware::image_projection_based_fusion + +#endif // AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__PREPROCESS_KERNEL_HPP_ diff --git a/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/pointpainting_fusion/voxel_generator.hpp b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/pointpainting_fusion/voxel_generator.hpp new file mode 100644 index 0000000000000..ac960aefd0bdb --- /dev/null +++ b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/pointpainting_fusion/voxel_generator.hpp @@ -0,0 +1,61 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__VOXEL_GENERATOR_HPP_ +#define AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__VOXEL_GENERATOR_HPP_ + +#include +<<<<<<<< + HEAD : perception / image_projection_based_fusion / include / autoware / + image_projection_based_fusion / pointpainting_fusion / voxel_generator.hpp +#include +#include + == == == == +#include +#include + >>>>>>>> original / + main : perception / autoware_image_projection_based_fusion / include / autoware / + image_projection_based_fusion / pointpainting_fusion / + voxel_generator.hpp + +#include +#include +#include + + namespace autoware::image_projection_based_fusion +{ + class VoxelGenerator + { + public: + explicit VoxelGenerator( + const autoware::lidar_centerpoint::DensificationParam & param, + const autoware::lidar_centerpoint::CenterPointConfig & config); + + bool enqueuePointCloud( + const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, + const tf2_ros::Buffer & tf_buffer); + + std::size_t generateSweepPoints(std::vector & points); + + protected: + std::unique_ptr pd_ptr_{nullptr}; + + autoware::lidar_centerpoint::CenterPointConfig config_; + std::array range_; + std::array grid_size_; + std::array recip_voxel_size_; + }; +} // namespace autoware::image_projection_based_fusion + +#endif // AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__VOXEL_GENERATOR_HPP_ diff --git a/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/roi_cluster_fusion/node.hpp b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/roi_cluster_fusion/node.hpp new file mode 100644 index 0000000000000..abececee35c25 --- /dev/null +++ b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/roi_cluster_fusion/node.hpp @@ -0,0 +1,66 @@ +// Copyright 2020 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__ROI_CLUSTER_FUSION__NODE_HPP_ +#define AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__ROI_CLUSTER_FUSION__NODE_HPP_ + +#include "autoware/image_projection_based_fusion/fusion_node.hpp" + +#include +#include +#include +namespace autoware::image_projection_based_fusion +{ +const std::map IOU_MODE_MAP{{"iou", 0}, {"iou_x", 1}, {"iou_y", 2}}; + +class RoiClusterFusionNode +: public FusionNode< + DetectedObjectsWithFeature, DetectedObjectWithFeature, DetectedObjectsWithFeature> +{ +public: + explicit RoiClusterFusionNode(const rclcpp::NodeOptions & options); + +protected: + void preprocess(DetectedObjectsWithFeature & output_cluster_msg) override; + void postprocess(DetectedObjectsWithFeature & output_cluster_msg) override; + + void fuseOnSingleImage( + const DetectedObjectsWithFeature & input_cluster_msg, const std::size_t image_id, + const DetectedObjectsWithFeature & input_roi_msg, + const sensor_msgs::msg::CameraInfo & camera_info, + DetectedObjectsWithFeature & output_cluster_msg) override; + + std::string trust_object_iou_mode_{"iou"}; + bool use_cluster_semantic_type_{false}; + bool only_allow_inside_cluster_{false}; + double roi_scale_factor_{1.1}; + double iou_threshold_{0.0}; + double unknown_iou_threshold_{0.0}; + const float min_roi_existence_prob_ = + 0.1; // keep small value to lessen affect on merger object stage + bool remove_unknown_; + double fusion_distance_; + double trust_object_distance_; + std::string non_trust_object_iou_mode_{"iou_x"}; + bool is_far_enough(const DetectedObjectWithFeature & obj, const double distance_threshold); + bool out_of_scope(const DetectedObjectWithFeature & obj); + double cal_iou_by_mode( + const sensor_msgs::msg::RegionOfInterest & roi_1, + const sensor_msgs::msg::RegionOfInterest & roi_2, const std::string iou_mode); + // bool CheckUnknown(const DetectedObjectsWithFeature & obj); +}; + +} // namespace autoware::image_projection_based_fusion + +#endif // AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__ROI_CLUSTER_FUSION__NODE_HPP_ diff --git a/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/roi_detected_object_fusion/node.hpp b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/roi_detected_object_fusion/node.hpp new file mode 100644 index 0000000000000..ea15a2df5efe2 --- /dev/null +++ b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/roi_detected_object_fusion/node.hpp @@ -0,0 +1,79 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__ROI_DETECTED_OBJECT_FUSION__NODE_HPP_ +#define AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__ROI_DETECTED_OBJECT_FUSION__NODE_HPP_ + +#include "autoware/image_projection_based_fusion/fusion_node.hpp" +#include "autoware/universe_utils/ros/debug_publisher.hpp" + +#include + +#include "autoware_perception_msgs/msg/object_classification.hpp" + +#include +#include +#include + +namespace autoware::image_projection_based_fusion +{ + +using sensor_msgs::msg::RegionOfInterest; + +class RoiDetectedObjectFusionNode +: public FusionNode +{ +public: + explicit RoiDetectedObjectFusionNode(const rclcpp::NodeOptions & options); + +protected: + void preprocess(DetectedObjects & output_msg) override; + + void fuseOnSingleImage( + const DetectedObjects & input_object_msg, const std::size_t image_id, + const DetectedObjectsWithFeature & input_roi_msg, + const sensor_msgs::msg::CameraInfo & camera_info, DetectedObjects & output_object_msg) override; + + std::map generateDetectedObjectRoIs( + const DetectedObjects & input_object_msg, const double image_width, const double image_height, + const Eigen::Affine3d & object2camera_affine, + const image_geometry::PinholeCameraModel & pinhole_camera_model); + + void fuseObjectsOnImage( + const DetectedObjects & input_object_msg, + const std::vector & image_rois, + const std::map & object_roi_map); + + void publish(const DetectedObjects & output_msg) override; + + bool out_of_scope(const DetectedObject & obj); + +private: + struct + { + std::vector passthrough_lower_bound_probability_thresholds{}; + std::vector trust_distances{}; + double min_iou_threshold{}; + bool use_roi_probability{}; + double roi_probability_threshold{}; + Eigen::MatrixXi can_assign_matrix; + } fusion_params_; + + std::map> passthrough_object_flags_map_, fused_object_flags_map_, + ignored_object_flags_map_; +}; + +} // namespace autoware::image_projection_based_fusion + +#endif // AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__ROI_DETECTED_OBJECT_FUSION__NODE_HPP_ diff --git a/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/roi_pointcloud_fusion/node.hpp b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/roi_pointcloud_fusion/node.hpp new file mode 100644 index 0000000000000..c7cad2b5a64d0 --- /dev/null +++ b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/roi_pointcloud_fusion/node.hpp @@ -0,0 +1,56 @@ +// Copyright 2023 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__ROI_POINTCLOUD_FUSION__NODE_HPP_ +#define AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__ROI_POINTCLOUD_FUSION__NODE_HPP_ + +#include "autoware/image_projection_based_fusion/fusion_node.hpp" + +#include + +#include +#include +namespace autoware::image_projection_based_fusion +{ +class RoiPointCloudFusionNode +: public FusionNode +{ +private: + int min_cluster_size_{1}; + int max_cluster_size_{20}; + bool fuse_unknown_only_{true}; + double cluster_2d_tolerance_; + + rclcpp::Publisher::SharedPtr pub_objects_ptr_; + std::vector output_fused_objects_; + rclcpp::Publisher::SharedPtr cluster_debug_pub_; + + /* data */ +public: + explicit RoiPointCloudFusionNode(const rclcpp::NodeOptions & options); + +protected: + void preprocess(sensor_msgs::msg::PointCloud2 & pointcloud_msg) override; + + void postprocess(sensor_msgs::msg::PointCloud2 & pointcloud_msg) override; + + void fuseOnSingleImage( + const PointCloud2 & input_pointcloud_msg, const std::size_t image_id, + const DetectedObjectsWithFeature & input_roi_msg, + const sensor_msgs::msg::CameraInfo & camera_info, PointCloud2 & output_pointcloud_msg) override; + bool out_of_scope(const DetectedObjectWithFeature & obj); +}; + +} // namespace autoware::image_projection_based_fusion +#endif // AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__ROI_POINTCLOUD_FUSION__NODE_HPP_ diff --git a/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/segmentation_pointcloud_fusion/node.hpp b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/segmentation_pointcloud_fusion/node.hpp new file mode 100644 index 0000000000000..c57d455300125 --- /dev/null +++ b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/segmentation_pointcloud_fusion/node.hpp @@ -0,0 +1,70 @@ +// Copyright 2023 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__SEGMENTATION_POINTCLOUD_FUSION__NODE_HPP_ +#define AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__SEGMENTATION_POINTCLOUD_FUSION__NODE_HPP_ + +#include "autoware/image_projection_based_fusion/fusion_node.hpp" + +#include + +#include +#include +#include + +#if __has_include() +#include +#else +#include +#endif + +namespace autoware::image_projection_based_fusion +{ +class SegmentPointCloudFusionNode : public FusionNode +{ +private: + rclcpp::Publisher::SharedPtr pub_pointcloud_ptr_; + std::vector filter_semantic_label_target_; + float filter_distance_threshold_; + // declare list of semantic label target, depend on trained data of yolox segmentation model + std::vector> filter_semantic_label_target_list_ = { + {"UNKNOWN", false}, {"BUILDING", false}, {"WALL", false}, {"OBSTACLE", false}, + {"TRAFFIC_LIGHT", false}, {"TRAFFIC_SIGN", false}, {"PERSON", false}, {"VEHICLE", false}, + {"BIKE", false}, {"ROAD", false}, {"SIDEWALK", false}, {"ROAD_PAINT", false}, + {"CURBSTONE", false}, {"CROSSWALK", false}, {"VEGETATION", false}, {"SKY", false}}; + +public: + explicit SegmentPointCloudFusionNode(const rclcpp::NodeOptions & options); + +protected: + void preprocess(PointCloud2 & pointcloud_msg) override; + + void postprocess(PointCloud2 & pointcloud_msg) override; + + void fuseOnSingleImage( + const PointCloud2 & input_pointcloud_msg, const std::size_t image_id, const Image & input_mask, + const CameraInfo & camera_info, PointCloud2 & output_pointcloud_msg) override; + + bool out_of_scope(const PointCloud2 & filtered_cloud); + inline void copyPointCloud( + const PointCloud2 & input, const int point_step, const size_t global_offset, + PointCloud2 & output, size_t & output_pointcloud_size) + { + std::memcpy(&output.data[output_pointcloud_size], &input.data[global_offset], point_step); + output_pointcloud_size += point_step; + } +}; + +} // namespace autoware::image_projection_based_fusion +#endif // AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__SEGMENTATION_POINTCLOUD_FUSION__NODE_HPP_ diff --git a/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/utils/geometry.hpp b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/utils/geometry.hpp new file mode 100644 index 0000000000000..25d6e1704b91a --- /dev/null +++ b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/utils/geometry.hpp @@ -0,0 +1,71 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__UTILS__GEOMETRY_HPP_ +#define AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__UTILS__GEOMETRY_HPP_ + +#define EIGEN_MPL2_ONLY + +#include +#include + +#include +#include +#include + +#include + +namespace autoware::image_projection_based_fusion +{ + +using autoware_perception_msgs::msg::Shape; +using geometry_msgs::msg::Pose; + +double calcIoU( + const sensor_msgs::msg::RegionOfInterest & roi_1, + const sensor_msgs::msg::RegionOfInterest & roi_2); + +double calcIoUX( + const sensor_msgs::msg::RegionOfInterest & roi_1, + const sensor_msgs::msg::RegionOfInterest & roi_2); + +double calcIoUY( + const sensor_msgs::msg::RegionOfInterest & roi_1, + const sensor_msgs::msg::RegionOfInterest & roi_2); + +void objectToVertices( + const Pose & pose, const Shape & shape, std::vector & vertices); + +void boundingBoxToVertices( + const Pose & pose, const Shape & shape, std::vector & vertices); + +void cylinderToVertices( + const Pose & pose, const Shape & shape, std::vector & vertices); + +void polygonToVertices( + const Pose & pose, const Shape & shape, std::vector & vertices); + +void transformPoints( + const std::vector & input_points, const Eigen::Affine3d & affine_transform, + std::vector & output_points); + +bool is_inside( + const sensor_msgs::msg::RegionOfInterest & outer, + const sensor_msgs::msg::RegionOfInterest & inner, const double outer_offset_scale = 1.1); + +void sanitizeROI(sensor_msgs::msg::RegionOfInterest & roi, const int width, const int height); + +} // namespace autoware::image_projection_based_fusion + +#endif // AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__UTILS__GEOMETRY_HPP_ diff --git a/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/utils/utils.hpp b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/utils/utils.hpp new file mode 100644 index 0000000000000..702b085dd31d7 --- /dev/null +++ b/perception/image_projection_based_fusion/include/autoware/image_projection_based_fusion/utils/utils.hpp @@ -0,0 +1,97 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__UTILS__UTILS_HPP_ +#define AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__UTILS__UTILS_HPP_ + +#define EIGEN_MPL2_ONLY + +#include +#include +#include + +#ifdef ROS_DISTRO_GALACTIC +#include +#include +#include +#else +#include + +#include +#include +#endif + +#include "autoware/image_projection_based_fusion/fusion_node.hpp" + +#include +#include +#include +#include + +#include "autoware_perception_msgs/msg/shape.hpp" +#include "tier4_perception_msgs/msg/detected_object_with_feature.hpp" + +#include +#include +#include +#include + +#include +#include +#include + +namespace autoware::image_projection_based_fusion +{ + +using PointCloud = pcl::PointCloud; +using PointCloud2 = sensor_msgs::msg::PointCloud2; +struct PointData +{ + float distance; + size_t orig_index; +}; + +Eigen::Vector2d calcRawImageProjectedPoint( + const image_geometry::PinholeCameraModel & pinhole_camera_model, const cv::Point3d & point3d); + +std::optional getTransformStamped( + const tf2_ros::Buffer & tf_buffer, const std::string & target_frame_id, + const std::string & source_frame_id, const rclcpp::Time & time); + +Eigen::Affine3d transformToEigen(const geometry_msgs::msg::Transform & t); + +void convertCluster2FeatureObject( + const std_msgs::msg::Header & header, const PointCloud & cluster, + DetectedObjectWithFeature & feature_obj); +void closest_cluster( + const PointCloud2 & cluster, const double cluster_2d_tolerance, const int min_cluster_size, + const pcl::PointXYZ & center, PointCloud2 & out_cluster); + +void updateOutputFusedObjects( + std::vector & output_objs, std::vector & clusters, + const std::vector clusters_data_size, const PointCloud2 & in_cloud, + const std_msgs::msg::Header & in_roi_header, const tf2_ros::Buffer & tf_buffer, + const int min_cluster_size, const int max_cluster_size, const float cluster_2d_tolerance, + std::vector & output_fused_objects); + +geometry_msgs::msg::Point getCentroid(const sensor_msgs::msg::PointCloud2 & pointcloud); + +pcl::PointXYZ getClosestPoint(const pcl::PointCloud & cluster); +void addShapeAndKinematic( + const pcl::PointCloud & cluster, + tier4_perception_msgs::msg::DetectedObjectWithFeature & feature_obj); + +} // namespace autoware::image_projection_based_fusion + +#endif // AUTOWARE__IMAGE_PROJECTION_BASED_FUSION__UTILS__UTILS_HPP_ diff --git a/perception/occupancy_grid_map_outlier_filter/src/occupancy_grid_map_outlier_filter_node.hpp b/perception/occupancy_grid_map_outlier_filter/src/occupancy_grid_map_outlier_filter_node.hpp index b7ec3ffeac6df..4c0f5f0e4cdbf 100644 --- a/perception/occupancy_grid_map_outlier_filter/src/occupancy_grid_map_outlier_filter_node.hpp +++ b/perception/occupancy_grid_map_outlier_filter/src/occupancy_grid_map_outlier_filter_node.hpp @@ -15,8 +15,13 @@ #ifndef OCCUPANCY_GRID_MAP_OUTLIER_FILTER_NODE_HPP_ #define OCCUPANCY_GRID_MAP_OUTLIER_FILTER_NODE_HPP_ +<<<<<<< HEAD +#include "autoware/universe_utils/ros/published_time_publisher.hpp" +#include "pointcloud_preprocessor/filter.hpp" + ======= #include "autoware/pointcloud_preprocessor/filter.hpp" #include "autoware/universe_utils/ros/published_time_publisher.hpp" + >>>>>>> original/main #include #include @@ -40,95 +45,96 @@ #include #include -namespace autoware::occupancy_grid_map_outlier_filter + namespace autoware::occupancy_grid_map_outlier_filter { -using geometry_msgs::msg::Pose; -using nav_msgs::msg::OccupancyGrid; -using sensor_msgs::msg::PointCloud2; -using std_msgs::msg::Header; + using geometry_msgs::msg::Pose; + using nav_msgs::msg::OccupancyGrid; + using sensor_msgs::msg::PointCloud2; + using std_msgs::msg::Header; -class RadiusSearch2dFilter -{ -public: - explicit RadiusSearch2dFilter(rclcpp::Node & node); - void filter( - const PointCloud2 & input, const Pose & pose, PointCloud2 & output, PointCloud2 & outlier); - void filter( - const PointCloud2 & high_conf_input, const PointCloud2 & low_conf_input, const Pose & pose, - PointCloud2 & output, PointCloud2 & outlier); - -private: - float search_radius_; - float min_points_and_distance_ratio_; - int min_points_; - int max_points_; - long unsigned int max_filter_points_nb_; - pcl::search::Search::Ptr kd_tree_; -}; - -class OccupancyGridMapOutlierFilterComponent : public rclcpp::Node -{ -public: - explicit OccupancyGridMapOutlierFilterComponent(const rclcpp::NodeOptions & options); - -private: - void onOccupancyGridMapAndPointCloud2( - const OccupancyGrid::ConstSharedPtr & input_occupancy_grid_map, - const PointCloud2::ConstSharedPtr & input_pointcloud); - void filterByOccupancyGridMap( - const OccupancyGrid & occupancy_grid_map, const PointCloud2 & pointcloud, - PointCloud2 & high_confidence, PointCloud2 & low_confidence, PointCloud2 & out_ogm); - void splitPointCloudFrontBack( - const PointCloud2::ConstSharedPtr & input_pc, PointCloud2 & front_pc, PointCloud2 & behind_pc); - void initializerPointCloud2(const PointCloud2 & input, PointCloud2 & output); - void finalizePointCloud2(const PointCloud2 & input, PointCloud2 & output); - void concatPointCloud2(PointCloud2 & output, const PointCloud2 & input); - -private: - class Debugger + class RadiusSearch2dFilter { public: - explicit Debugger(OccupancyGridMapOutlierFilterComponent & node); - void publishOutlier(const PointCloud2 & input, const Header & header); - void publishHighConfidence(const PointCloud2 & input, const Header & header); - void publishLowConfidence(const PointCloud2 & input, const Header & header); + explicit RadiusSearch2dFilter(rclcpp::Node & node); + void filter( + const PointCloud2 & input, const Pose & pose, PointCloud2 & output, PointCloud2 & outlier); + void filter( + const PointCloud2 & high_conf_input, const PointCloud2 & low_conf_input, const Pose & pose, + PointCloud2 & output, PointCloud2 & outlier); private: - void transformToBaseLink( - const PointCloud2 & input, const Header & header, PointCloud2 & output); - rclcpp::Publisher::SharedPtr outlier_pointcloud_pub_; - rclcpp::Publisher::SharedPtr low_confidence_pointcloud_pub_; - rclcpp::Publisher::SharedPtr high_confidence_pointcloud_pub_; - const OccupancyGridMapOutlierFilterComponent & node_; + float search_radius_; + float min_points_and_distance_ratio_; + int min_points_; + int max_points_; + long unsigned int max_filter_points_nb_; + pcl::search::Search::Ptr kd_tree_; }; -private: - // publishers and subscribers - rclcpp::Publisher::SharedPtr pointcloud_pub_; - message_filters::Subscriber occupancy_grid_map_sub_; - message_filters::Subscriber pointcloud_sub_; - using SyncPolicy = message_filters::sync_policies::ExactTime; - using Sync = message_filters::Synchronizer; - std::shared_ptr sync_ptr_; - - // tf - std::shared_ptr tf2_; - std::shared_ptr tf2_listener_; - - // 2d outlier filter - std::shared_ptr radius_search_2d_filter_ptr_; - - // Debugger - std::shared_ptr debugger_ptr_; - std::unique_ptr> stop_watch_ptr_; - std::unique_ptr debug_publisher_; - std::unique_ptr published_time_publisher_; - - // ROS Parameters - std::string map_frame_; - std::string base_link_frame_; - int cost_threshold_; -}; + class OccupancyGridMapOutlierFilterComponent : public rclcpp::Node + { + public: + explicit OccupancyGridMapOutlierFilterComponent(const rclcpp::NodeOptions & options); + + private: + void onOccupancyGridMapAndPointCloud2( + const OccupancyGrid::ConstSharedPtr & input_occupancy_grid_map, + const PointCloud2::ConstSharedPtr & input_pointcloud); + void filterByOccupancyGridMap( + const OccupancyGrid & occupancy_grid_map, const PointCloud2 & pointcloud, + PointCloud2 & high_confidence, PointCloud2 & low_confidence, PointCloud2 & out_ogm); + void splitPointCloudFrontBack( + const PointCloud2::ConstSharedPtr & input_pc, PointCloud2 & front_pc, + PointCloud2 & behind_pc); + void initializerPointCloud2(const PointCloud2 & input, PointCloud2 & output); + void finalizePointCloud2(const PointCloud2 & input, PointCloud2 & output); + void concatPointCloud2(PointCloud2 & output, const PointCloud2 & input); + + private: + class Debugger + { + public: + explicit Debugger(OccupancyGridMapOutlierFilterComponent & node); + void publishOutlier(const PointCloud2 & input, const Header & header); + void publishHighConfidence(const PointCloud2 & input, const Header & header); + void publishLowConfidence(const PointCloud2 & input, const Header & header); + + private: + void transformToBaseLink( + const PointCloud2 & input, const Header & header, PointCloud2 & output); + rclcpp::Publisher::SharedPtr outlier_pointcloud_pub_; + rclcpp::Publisher::SharedPtr low_confidence_pointcloud_pub_; + rclcpp::Publisher::SharedPtr high_confidence_pointcloud_pub_; + const OccupancyGridMapOutlierFilterComponent & node_; + }; + + private: + // publishers and subscribers + rclcpp::Publisher::SharedPtr pointcloud_pub_; + message_filters::Subscriber occupancy_grid_map_sub_; + message_filters::Subscriber pointcloud_sub_; + using SyncPolicy = message_filters::sync_policies::ExactTime; + using Sync = message_filters::Synchronizer; + std::shared_ptr sync_ptr_; + + // tf + std::shared_ptr tf2_; + std::shared_ptr tf2_listener_; + + // 2d outlier filter + std::shared_ptr radius_search_2d_filter_ptr_; + + // Debugger + std::shared_ptr debugger_ptr_; + std::unique_ptr> stop_watch_ptr_; + std::unique_ptr debug_publisher_; + std::unique_ptr published_time_publisher_; + + // ROS Parameters + std::string map_frame_; + std::string base_link_frame_; + int cost_threshold_; + }; } // namespace autoware::occupancy_grid_map_outlier_filter #endif // OCCUPANCY_GRID_MAP_OUTLIER_FILTER_NODE_HPP_ diff --git a/perception/probabilistic_occupancy_grid_map/lib/costmap_2d/occupancy_grid_map_fixed.cpp b/perception/probabilistic_occupancy_grid_map/lib/costmap_2d/occupancy_grid_map_fixed.cpp index b940b932db0ef..83deb10adc882 100644 --- a/perception/probabilistic_occupancy_grid_map/lib/costmap_2d/occupancy_grid_map_fixed.cpp +++ b/perception/probabilistic_occupancy_grid_map/lib/costmap_2d/occupancy_grid_map_fixed.cpp @@ -232,7 +232,11 @@ void OccupancyGridMapFixedBlindSpot::updateWithPointCloud( } // Third step: Overwrite occupied cell +<<<<<<< HEAD + for (auto & obstacle_pointcloud_angle_bin : obstacle_pointcloud_angle_bins) { +======= for (const auto & obstacle_pointcloud_angle_bin : obstacle_pointcloud_angle_bins) { +>>>>>>> original/main for (size_t dist_index = 0; dist_index < obstacle_pointcloud_angle_bin.size(); ++dist_index) { const auto & obstacle_point = obstacle_pointcloud_angle_bin.at(dist_index); setCellValue(obstacle_point.wx, obstacle_point.wy, cost_value::LETHAL_OBSTACLE); diff --git a/perception/probabilistic_occupancy_grid_map/lib/costmap_2d/occupancy_grid_map_projective.cpp b/perception/probabilistic_occupancy_grid_map/lib/costmap_2d/occupancy_grid_map_projective.cpp index dd934dea26c55..9ee6e30841b54 100644 --- a/perception/probabilistic_occupancy_grid_map/lib/costmap_2d/occupancy_grid_map_projective.cpp +++ b/perception/probabilistic_occupancy_grid_map/lib/costmap_2d/occupancy_grid_map_projective.cpp @@ -285,7 +285,11 @@ void OccupancyGridMapProjectiveBlindSpot::updateWithPointCloud( if (pub_debug_grid_) converter.addLayerFromCostmap2D(*this, "added_unknown", debug_grid_); // Third step: Overwrite occupied cell +<<<<<<< HEAD + for (auto & obstacle_pointcloud_angle_bin : obstacle_pointcloud_angle_bins) { +======= for (const auto & obstacle_pointcloud_angle_bin : obstacle_pointcloud_angle_bins) { +>>>>>>> original/main for (size_t dist_index = 0; dist_index < obstacle_pointcloud_angle_bin.size(); ++dist_index) { const auto & obstacle_point = obstacle_pointcloud_angle_bin.at(dist_index); setCellValue(obstacle_point.wx, obstacle_point.wy, cost_value::LETHAL_OBSTACLE); diff --git a/perception/shape_estimation/include/autoware/shape_estimation/corrector/bicycle_corrector.hpp b/perception/shape_estimation/include/autoware/shape_estimation/corrector/bicycle_corrector.hpp new file mode 100644 index 0000000000000..f8dc489a563be --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/corrector/bicycle_corrector.hpp @@ -0,0 +1,48 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__BICYCLE_CORRECTOR_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__BICYCLE_CORRECTOR_HPP_ + +#include "autoware/shape_estimation/corrector/vehicle_corrector.hpp" +#include "utils.hpp" + +namespace autoware::shape_estimation +{ +namespace corrector +{ + +class BicycleCorrector : public VehicleCorrector +{ +public: + explicit BicycleCorrector(const bool use_reference_yaw = false) + : VehicleCorrector(use_reference_yaw) + { + corrector_utils::CorrectionBBParameters params; + params.min_width = 0.3; + params.max_width = 1.2; + params.default_width = (params.min_width + params.max_width) * 0.5; + params.min_length = 1.0; + params.max_length = 2.8; + params.default_length = (params.min_length + params.max_length) * 0.5; + setParams(params); + } + + ~BicycleCorrector() = default; +}; + +} // namespace corrector +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__BICYCLE_CORRECTOR_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/corrector/bus_corrector.hpp b/perception/shape_estimation/include/autoware/shape_estimation/corrector/bus_corrector.hpp new file mode 100644 index 0000000000000..d7386ace5b364 --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/corrector/bus_corrector.hpp @@ -0,0 +1,45 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__BUS_CORRECTOR_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__BUS_CORRECTOR_HPP_ + +#include "autoware/shape_estimation/corrector/vehicle_corrector.hpp" +#include "utils.hpp" +namespace autoware::shape_estimation +{ +namespace corrector +{ +class BusCorrector : public VehicleCorrector +{ +public: + explicit BusCorrector(const bool use_reference_yaw = false) : VehicleCorrector(use_reference_yaw) + { + corrector_utils::CorrectionBBParameters params; + params.min_width = 2.0; + params.max_width = 3.2; + params.default_width = (params.min_width + params.max_width) * 0.5; + params.min_length = 5.0; + params.max_length = 17.0; + params.default_length = 7.0; + setParams(params); + } + + ~BusCorrector() = default; +}; + +} // namespace corrector +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__BUS_CORRECTOR_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/corrector/car_corrector.hpp b/perception/shape_estimation/include/autoware/shape_estimation/corrector/car_corrector.hpp new file mode 100644 index 0000000000000..7292704d0f6d9 --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/corrector/car_corrector.hpp @@ -0,0 +1,47 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__CAR_CORRECTOR_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__CAR_CORRECTOR_HPP_ + +#include "autoware/shape_estimation/corrector/vehicle_corrector.hpp" +#include "utils.hpp" + +namespace autoware::shape_estimation +{ +namespace corrector +{ + +class CarCorrector : public VehicleCorrector +{ +public: + explicit CarCorrector(const bool use_reference_yaw = false) : VehicleCorrector(use_reference_yaw) + { + corrector_utils::CorrectionBBParameters params; + params.min_width = 1.2; + params.max_width = 2.5; + params.default_width = (params.min_width + params.max_width) * 0.5; + params.min_length = 3.0; + params.max_length = 5.8; + params.default_length = (params.min_length + params.max_length) * 0.5; + setParams(params); + } + + ~CarCorrector() = default; +}; + +} // namespace corrector +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__CAR_CORRECTOR_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/corrector/corrector.hpp b/perception/shape_estimation/include/autoware/shape_estimation/corrector/corrector.hpp new file mode 100644 index 0000000000000..dd4c4f68d3f46 --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/corrector/corrector.hpp @@ -0,0 +1,27 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__CORRECTOR_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__CORRECTOR_HPP_ + +#include "autoware/shape_estimation/corrector/bicycle_corrector.hpp" +#include "autoware/shape_estimation/corrector/bus_corrector.hpp" +#include "autoware/shape_estimation/corrector/car_corrector.hpp" +#include "autoware/shape_estimation/corrector/corrector_interface.hpp" +#include "autoware/shape_estimation/corrector/no_corrector.hpp" +#include "autoware/shape_estimation/corrector/reference_shape_size_corrector.hpp" +#include "autoware/shape_estimation/corrector/trailer_corrector.hpp" +#include "autoware/shape_estimation/corrector/truck_corrector.hpp" + +#endif // AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__CORRECTOR_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/corrector/corrector_interface.hpp b/perception/shape_estimation/include/autoware/shape_estimation/corrector/corrector_interface.hpp new file mode 100644 index 0000000000000..1dcf85b46887c --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/corrector/corrector_interface.hpp @@ -0,0 +1,40 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__CORRECTOR_INTERFACE_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__CORRECTOR_INTERFACE_HPP_ + +#include +#include + +#include +namespace autoware::shape_estimation +{ +namespace corrector +{ +class ShapeEstimationCorrectorInterface +{ +public: + ShapeEstimationCorrectorInterface() {} + + virtual ~ShapeEstimationCorrectorInterface() {} + + virtual bool correct( + autoware_perception_msgs::msg::Shape & shape, geometry_msgs::msg::Pose & pose) = 0; +}; + +} // namespace corrector +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__CORRECTOR_INTERFACE_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/corrector/no_corrector.hpp b/perception/shape_estimation/include/autoware/shape_estimation/corrector/no_corrector.hpp new file mode 100644 index 0000000000000..e3c33100861a0 --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/corrector/no_corrector.hpp @@ -0,0 +1,44 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__NO_CORRECTOR_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__NO_CORRECTOR_HPP_ + +#include "autoware/shape_estimation/corrector/corrector_interface.hpp" +#include "utils.hpp" + +namespace autoware::shape_estimation +{ +namespace corrector +{ + +class NoCorrector : public ShapeEstimationCorrectorInterface +{ +public: + NoCorrector() {} + + ~NoCorrector() {} + + bool correct( + [[maybe_unused]] autoware_perception_msgs::msg::Shape & shape, + [[maybe_unused]] geometry_msgs::msg::Pose & pose) override + { + return true; + } +}; + +} // namespace corrector +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__NO_CORRECTOR_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/corrector/reference_shape_size_corrector.hpp b/perception/shape_estimation/include/autoware/shape_estimation/corrector/reference_shape_size_corrector.hpp new file mode 100644 index 0000000000000..a05027a3348b6 --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/corrector/reference_shape_size_corrector.hpp @@ -0,0 +1,48 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__REFERENCE_SHAPE_SIZE_CORRECTOR_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__REFERENCE_SHAPE_SIZE_CORRECTOR_HPP_ + +#include "autoware/shape_estimation/corrector/corrector_interface.hpp" +#include "autoware/shape_estimation/shape_estimator.hpp" +#include "utils.hpp" + +namespace autoware::shape_estimation +{ +namespace corrector +{ + +class ReferenceShapeBasedVehicleCorrector : public ShapeEstimationCorrectorInterface +{ + ReferenceShapeSizeInfo ref_shape_size_info_; + +public: + explicit ReferenceShapeBasedVehicleCorrector(const ReferenceShapeSizeInfo & ref_shape_size_info) + : ref_shape_size_info_(ref_shape_size_info) + { + } + + virtual ~ReferenceShapeBasedVehicleCorrector() = default; + + bool correct( + autoware_perception_msgs::msg::Shape & shape, geometry_msgs::msg::Pose & pose) override + { + return corrector_utils::correctWithReferenceYawAndShapeSize(ref_shape_size_info_, shape, pose); + } +}; + +} // namespace corrector +} // namespace autoware::shape_estimation +#endif // AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__REFERENCE_SHAPE_SIZE_CORRECTOR_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/corrector/trailer_corrector.hpp b/perception/shape_estimation/include/autoware/shape_estimation/corrector/trailer_corrector.hpp new file mode 100644 index 0000000000000..afd0f1f7c451d --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/corrector/trailer_corrector.hpp @@ -0,0 +1,51 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__TRAILER_CORRECTOR_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__TRAILER_CORRECTOR_HPP_ + +#include "autoware/shape_estimation/corrector/vehicle_corrector.hpp" +#include "utils.hpp" + +namespace autoware::shape_estimation +{ +namespace corrector +{ + +// Generally speaking, trailer would be much larger than bus and truck. +// But currently we do not make large differences among bus/truck/trailer +// because current our vehicle classification is not reliable enough. +class TrailerCorrector : public VehicleCorrector +{ +public: + explicit TrailerCorrector(const bool use_reference_yaw = false) + : VehicleCorrector(use_reference_yaw) + { + corrector_utils::CorrectionBBParameters params; + params.min_width = 2.0; + params.max_width = 3.2; + params.default_width = (params.min_width + params.max_width) * 0.5; + params.min_length = 5.0; + params.max_length = 24.0; + params.default_length = 8.0; // Ideally, it should be 12m from average Japanese trailer size. + setParams(params); + } + + ~TrailerCorrector() = default; +}; + +} // namespace corrector +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__TRAILER_CORRECTOR_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/corrector/truck_corrector.hpp b/perception/shape_estimation/include/autoware/shape_estimation/corrector/truck_corrector.hpp new file mode 100644 index 0000000000000..8a641e84176a5 --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/corrector/truck_corrector.hpp @@ -0,0 +1,48 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__TRUCK_CORRECTOR_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__TRUCK_CORRECTOR_HPP_ + +#include "autoware/shape_estimation/corrector/vehicle_corrector.hpp" +#include "utils.hpp" + +namespace autoware::shape_estimation +{ +namespace corrector +{ + +class TruckCorrector : public VehicleCorrector +{ +public: + explicit TruckCorrector(const bool use_reference_yaw = false) + : VehicleCorrector(use_reference_yaw) + { + corrector_utils::CorrectionBBParameters params; + params.min_width = 1.5; + params.max_width = 3.5; + params.default_width = (params.min_width + params.max_width) * 0.5; + params.min_length = 4.0; + params.max_length = 18.0; + params.default_length = 7.0; // 7m is the most common length of a truck in Japan + setParams(params); + } + + ~TruckCorrector() = default; +}; + +} // namespace corrector +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__TRUCK_CORRECTOR_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/corrector/utils.hpp b/perception/shape_estimation/include/autoware/shape_estimation/corrector/utils.hpp new file mode 100644 index 0000000000000..a16cb146ca4a3 --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/corrector/utils.hpp @@ -0,0 +1,54 @@ +// Copyright 2021 TierIV. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__UTILS_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__UTILS_HPP_ + +#include "autoware/shape_estimation/shape_estimator.hpp" + +#include +#include + +namespace autoware::shape_estimation +{ + +namespace corrector_utils +{ +struct CorrectionBBParameters +{ + double min_width; + double max_width; + double default_width; + double min_length; + double max_length; + double default_length; +}; + +bool correctWithDefaultValue( + const CorrectionBBParameters & param, autoware_perception_msgs::msg::Shape & shape_output, + geometry_msgs::msg::Pose & pose_output); + +bool correctWithReferenceYawAndShapeSize( + const ReferenceShapeSizeInfo & ref_shape_size_info, + autoware_perception_msgs::msg::Shape & shape_output, geometry_msgs::msg::Pose & pose_output); + +bool correctWithReferenceYaw( + const CorrectionBBParameters & param, autoware_perception_msgs::msg::Shape & shape_output, + geometry_msgs::msg::Pose & pose_output); + +} // namespace corrector_utils + +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__UTILS_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/corrector/vehicle_corrector.hpp b/perception/shape_estimation/include/autoware/shape_estimation/corrector/vehicle_corrector.hpp new file mode 100644 index 0000000000000..e1b292eb5318a --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/corrector/vehicle_corrector.hpp @@ -0,0 +1,58 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__VEHICLE_CORRECTOR_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__VEHICLE_CORRECTOR_HPP_ + +#include "autoware/shape_estimation/corrector/corrector_interface.hpp" +#include "autoware/shape_estimation/shape_estimator.hpp" +#include "utils.hpp" + +namespace autoware::shape_estimation +{ +namespace corrector +{ + +class VehicleCorrector : public ShapeEstimationCorrectorInterface +{ +protected: + boost::optional params_; + +private: + bool use_reference_yaw_; + +public: + explicit VehicleCorrector(const bool use_reference_yaw) : use_reference_yaw_(use_reference_yaw) {} + + virtual ~VehicleCorrector() = default; + + bool correct( + autoware_perception_msgs::msg::Shape & shape, geometry_msgs::msg::Pose & pose) override + { + // Guard + if (!params_) return false; + + if (use_reference_yaw_) + return corrector_utils::correctWithReferenceYaw(params_.get(), shape, pose); + else + return correctWithDefaultValue(params_.get(), shape, pose); + } + + void setParams(const corrector_utils::CorrectionBBParameters & params) { params_ = params; } +}; + +} // namespace corrector +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__CORRECTOR__VEHICLE_CORRECTOR_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/filter/bus_filter.hpp b/perception/shape_estimation/include/autoware/shape_estimation/filter/bus_filter.hpp new file mode 100644 index 0000000000000..54d45498a3d40 --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/filter/bus_filter.hpp @@ -0,0 +1,40 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__FILTER__BUS_FILTER_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__FILTER__BUS_FILTER_HPP_ + +#include "autoware/shape_estimation/filter/filter_interface.hpp" +#include "utils.hpp" + +namespace autoware::shape_estimation +{ +namespace filter +{ +class BusFilter : public ShapeEstimationFilterInterface +{ +public: + BusFilter() = default; + + ~BusFilter() = default; + + bool filter( + const autoware_perception_msgs::msg::Shape & shape_output, + const geometry_msgs::msg::Pose & pose_output) override; +}; + +} // namespace filter +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__FILTER__BUS_FILTER_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/filter/car_filter.hpp b/perception/shape_estimation/include/autoware/shape_estimation/filter/car_filter.hpp new file mode 100644 index 0000000000000..8ff79f901d8a3 --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/filter/car_filter.hpp @@ -0,0 +1,40 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__FILTER__CAR_FILTER_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__FILTER__CAR_FILTER_HPP_ + +#include "autoware/shape_estimation/filter/filter_interface.hpp" +#include "utils.hpp" + +namespace autoware::shape_estimation +{ +namespace filter +{ +class CarFilter : public ShapeEstimationFilterInterface +{ +public: + CarFilter() = default; + + ~CarFilter() = default; + + bool filter( + const autoware_perception_msgs::msg::Shape & shape, + const geometry_msgs::msg::Pose & pose) override; +}; + +} // namespace filter +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__FILTER__CAR_FILTER_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/filter/filter.hpp b/perception/shape_estimation/include/autoware/shape_estimation/filter/filter.hpp new file mode 100644 index 0000000000000..5b7bad4f57208 --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/filter/filter.hpp @@ -0,0 +1,25 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__FILTER__FILTER_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__FILTER__FILTER_HPP_ + +#include "autoware/shape_estimation/filter/bus_filter.hpp" +#include "autoware/shape_estimation/filter/car_filter.hpp" +#include "autoware/shape_estimation/filter/filter_interface.hpp" +#include "autoware/shape_estimation/filter/no_filter.hpp" +#include "autoware/shape_estimation/filter/trailer_filter.hpp" +#include "autoware/shape_estimation/filter/truck_filter.hpp" + +#endif // AUTOWARE__SHAPE_ESTIMATION__FILTER__FILTER_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/filter/filter_interface.hpp b/perception/shape_estimation/include/autoware/shape_estimation/filter/filter_interface.hpp new file mode 100644 index 0000000000000..cba7715314a9e --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/filter/filter_interface.hpp @@ -0,0 +1,42 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__FILTER__FILTER_INTERFACE_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__FILTER__FILTER_INTERFACE_HPP_ + +#include +#include + +#include + +namespace autoware::shape_estimation +{ +namespace filter +{ + +class ShapeEstimationFilterInterface +{ +public: + ShapeEstimationFilterInterface() {} + + virtual ~ShapeEstimationFilterInterface() {} + + virtual bool filter( + const autoware_perception_msgs::msg::Shape & shape, const geometry_msgs::msg::Pose & pose) = 0; +}; + +} // namespace filter +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__FILTER__FILTER_INTERFACE_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/filter/no_filter.hpp b/perception/shape_estimation/include/autoware/shape_estimation/filter/no_filter.hpp new file mode 100644 index 0000000000000..f05c0e9affa02 --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/filter/no_filter.hpp @@ -0,0 +1,43 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__FILTER__NO_FILTER_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__FILTER__NO_FILTER_HPP_ + +#include "autoware/shape_estimation/filter/filter_interface.hpp" + +namespace autoware::shape_estimation +{ +namespace filter +{ + +class NoFilter : public ShapeEstimationFilterInterface +{ +public: + NoFilter() = default; + + ~NoFilter() = default; + + bool filter( + [[maybe_unused]] const autoware_perception_msgs::msg::Shape & shape, + [[maybe_unused]] const geometry_msgs::msg::Pose & pose) override + { + return true; + } +}; + +} // namespace filter +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__FILTER__NO_FILTER_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/filter/trailer_filter.hpp b/perception/shape_estimation/include/autoware/shape_estimation/filter/trailer_filter.hpp new file mode 100644 index 0000000000000..21394938a8026 --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/filter/trailer_filter.hpp @@ -0,0 +1,39 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__FILTER__TRAILER_FILTER_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__FILTER__TRAILER_FILTER_HPP_ + +#include "autoware/shape_estimation/filter/filter_interface.hpp" +#include "utils.hpp" + +namespace autoware::shape_estimation +{ +namespace filter +{ +class TrailerFilter : public ShapeEstimationFilterInterface +{ +public: + TrailerFilter() = default; + + ~TrailerFilter() = default; + + bool filter( + const autoware_perception_msgs::msg::Shape & shape, + const geometry_msgs::msg::Pose & pose) override; +}; +} // namespace filter +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__FILTER__TRAILER_FILTER_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/filter/truck_filter.hpp b/perception/shape_estimation/include/autoware/shape_estimation/filter/truck_filter.hpp new file mode 100644 index 0000000000000..a941ec4fd7932 --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/filter/truck_filter.hpp @@ -0,0 +1,38 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__FILTER__TRUCK_FILTER_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__FILTER__TRUCK_FILTER_HPP_ + +#include "autoware/shape_estimation/filter/filter_interface.hpp" +#include "utils.hpp" +namespace autoware::shape_estimation +{ +namespace filter +{ +class TruckFilter : public ShapeEstimationFilterInterface +{ +public: + TruckFilter() = default; + + ~TruckFilter() = default; + + bool filter( + const autoware_perception_msgs::msg::Shape & shape, + const geometry_msgs::msg::Pose & pose) override; +}; +} // namespace filter +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__FILTER__TRUCK_FILTER_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/filter/utils.hpp b/perception/shape_estimation/include/autoware/shape_estimation/filter/utils.hpp new file mode 100644 index 0000000000000..7f56664e0a57d --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/filter/utils.hpp @@ -0,0 +1,31 @@ +// Copyright 2021 TierIV. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__FILTER__UTILS_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__FILTER__UTILS_HPP_ + +#include +namespace autoware::shape_estimation +{ + +namespace utils +{ +bool filterVehicleBoundingBox( + const autoware_perception_msgs::msg::Shape & shape, const float min_width, const float max_width, + const float max_length); +} // namespace utils + +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__FILTER__UTILS_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/model/bounding_box.hpp b/perception/shape_estimation/include/autoware/shape_estimation/model/bounding_box.hpp new file mode 100644 index 0000000000000..c9d6889c21de5 --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/model/bounding_box.hpp @@ -0,0 +1,58 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__MODEL__BOUNDING_BOX_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__MODEL__BOUNDING_BOX_HPP_ + +#include "autoware/shape_estimation/model/model_interface.hpp" +#include "autoware/shape_estimation/shape_estimator.hpp" + +#include + +namespace autoware::shape_estimation +{ +namespace model +{ + +class BoundingBoxShapeModel : public ShapeEstimationModelInterface +{ +private: + bool fitLShape( + const pcl::PointCloud & cluster, const float min_angle, const float max_angle, + autoware_perception_msgs::msg::Shape & shape_output, geometry_msgs::msg::Pose & pose_output); + float calcClosenessCriterion(const std::vector & C_1, const std::vector & C_2); + float optimize( + const pcl::PointCloud & cluster, const float min_angle, const float max_angle); + float boostOptimize( + const pcl::PointCloud & cluster, const float min_angle, const float max_angle); + +public: + BoundingBoxShapeModel(); + explicit BoundingBoxShapeModel( + const boost::optional & ref_yaw_info, bool use_boost_bbox_optimizer = false); + boost::optional ref_yaw_info_; + bool use_boost_bbox_optimizer_; + + ~BoundingBoxShapeModel() {} + + bool estimate( + const pcl::PointCloud & cluster, + autoware_perception_msgs::msg::Shape & shape_output, + geometry_msgs::msg::Pose & pose_output) override; +}; + +} // namespace model +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__MODEL__BOUNDING_BOX_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/model/convex_hull.hpp b/perception/shape_estimation/include/autoware/shape_estimation/model/convex_hull.hpp new file mode 100644 index 0000000000000..c3ffa297833fb --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/model/convex_hull.hpp @@ -0,0 +1,41 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__MODEL__CONVEX_HULL_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__MODEL__CONVEX_HULL_HPP_ + +#include "autoware/shape_estimation/model/model_interface.hpp" + +namespace autoware::shape_estimation +{ +namespace model +{ + +class ConvexHullShapeModel : public ShapeEstimationModelInterface +{ +public: + ConvexHullShapeModel() {} + + ~ConvexHullShapeModel() {} + + bool estimate( + const pcl::PointCloud & cluster, + autoware_perception_msgs::msg::Shape & shape_output, + geometry_msgs::msg::Pose & pose_output) override; +}; + +} // namespace model +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__MODEL__CONVEX_HULL_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/model/cylinder.hpp b/perception/shape_estimation/include/autoware/shape_estimation/model/cylinder.hpp new file mode 100644 index 0000000000000..6623934cdcc21 --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/model/cylinder.hpp @@ -0,0 +1,44 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__MODEL__CYLINDER_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__MODEL__CYLINDER_HPP_ + +#include "autoware/shape_estimation/model/model_interface.hpp" + +namespace autoware::shape_estimation +{ +namespace model +{ + +class CylinderShapeModel : public ShapeEstimationModelInterface +{ +public: + CylinderShapeModel() {} + + ~CylinderShapeModel() {} + + /* + * if cluster size is 1, it can be calculated using the lower limit of the size of the object + */ + bool estimate( + const pcl::PointCloud & cluster, + autoware_perception_msgs::msg::Shape & shape_output, + geometry_msgs::msg::Pose & pose_output) override; +}; + +} // namespace model +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__MODEL__CYLINDER_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/model/model.hpp b/perception/shape_estimation/include/autoware/shape_estimation/model/model.hpp new file mode 100644 index 0000000000000..382812de6f188 --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/model/model.hpp @@ -0,0 +1,23 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__MODEL__MODEL_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__MODEL__MODEL_HPP_ + +#include "autoware/shape_estimation/model/bounding_box.hpp" +#include "autoware/shape_estimation/model/convex_hull.hpp" +#include "autoware/shape_estimation/model/cylinder.hpp" +#include "autoware/shape_estimation/model/model_interface.hpp" + +#endif // AUTOWARE__SHAPE_ESTIMATION__MODEL__MODEL_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/model/model_interface.hpp b/perception/shape_estimation/include/autoware/shape_estimation/model/model_interface.hpp new file mode 100644 index 0000000000000..380e88704baae --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/model/model_interface.hpp @@ -0,0 +1,48 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__MODEL__MODEL_INTERFACE_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__MODEL__MODEL_INTERFACE_HPP_ + +#include +#include + +#include +#include +#include + +#include + +namespace autoware::shape_estimation +{ +namespace model +{ + +class ShapeEstimationModelInterface +{ +public: + ShapeEstimationModelInterface() {} + + virtual ~ShapeEstimationModelInterface() {} + + virtual bool estimate( + const pcl::PointCloud & cluster, + autoware_perception_msgs::msg::Shape & shape_output, + geometry_msgs::msg::Pose & pose_output) = 0; +}; + +} // namespace model +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__MODEL__MODEL_INTERFACE_HPP_ diff --git a/perception/shape_estimation/include/autoware/shape_estimation/shape_estimator.hpp b/perception/shape_estimation/include/autoware/shape_estimation/shape_estimator.hpp new file mode 100644 index 0000000000000..9759c7c77855e --- /dev/null +++ b/perception/shape_estimation/include/autoware/shape_estimation/shape_estimator.hpp @@ -0,0 +1,83 @@ +// Copyright 2018 Autoware Foundation. All rights reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__SHAPE_ESTIMATION__SHAPE_ESTIMATOR_HPP_ +#define AUTOWARE__SHAPE_ESTIMATION__SHAPE_ESTIMATOR_HPP_ + +#include +#include +#include + +#include + +#include +#include +#include + +#include + +namespace autoware::shape_estimation +{ + +struct ReferenceYawInfo +{ + float yaw; + float search_angle_range; +}; + +struct ReferenceShapeSizeInfo +{ + autoware_perception_msgs::msg::Shape shape; + /** + * @brief + * Fix mode : use the reference shape size as fixed value. + * Min mode : use the reference shape size as minimum value. + */ + enum Mode { Fix, Min }; + Mode mode; +}; + +class ShapeEstimator +{ +private: + bool estimateOriginalShapeAndPose( + const uint8_t label, const pcl::PointCloud & cluster, + const boost::optional & ref_yaw_info, + autoware_perception_msgs::msg::Shape & shape_output, geometry_msgs::msg::Pose & pose_output); + bool applyFilter( + const uint8_t label, const autoware_perception_msgs::msg::Shape & shape, + const geometry_msgs::msg::Pose & pose); + bool applyCorrector( + const uint8_t label, const bool use_reference_yaw, + const boost::optional & ref_shape_size_info, + autoware_perception_msgs::msg::Shape & shape, geometry_msgs::msg::Pose & pose); + + bool use_corrector_; + bool use_filter_; + bool use_boost_bbox_optimizer_; + +public: + ShapeEstimator(bool use_corrector, bool use_filter, bool use_boost_bbox_optimizer = false); + + virtual ~ShapeEstimator() = default; + + virtual bool estimateShapeAndPose( + const uint8_t label, const pcl::PointCloud & cluster, + const boost::optional & ref_yaw_info, + const boost::optional & ref_shape_size_info, + autoware_perception_msgs::msg::Shape & shape_output, geometry_msgs::msg::Pose & pose_output); +}; +} // namespace autoware::shape_estimation + +#endif // AUTOWARE__SHAPE_ESTIMATION__SHAPE_ESTIMATOR_HPP_ diff --git a/planning/autoware_path_optimizer/include/autoware/path_optimizer/node.hpp b/planning/autoware_path_optimizer/include/autoware/path_optimizer/node.hpp index cad3a9fc89340..d6c2b4dceab61 100644 --- a/planning/autoware_path_optimizer/include/autoware/path_optimizer/node.hpp +++ b/planning/autoware_path_optimizer/include/autoware/path_optimizer/node.hpp @@ -22,7 +22,10 @@ #include "autoware/path_optimizer/type_alias.hpp" #include "autoware/universe_utils/ros/logger_level_configure.hpp" #include "autoware/universe_utils/ros/polling_subscriber.hpp" +<<<<<<< HEAD +======= #include "autoware/universe_utils/system/stop_watch.hpp" +>>>>>>> original/main #include "autoware/universe_utils/system/time_keeper.hpp" #include "autoware_vehicle_info_utils/vehicle_info_utils.hpp" @@ -35,118 +38,118 @@ #include #include -namespace autoware::path_optimizer + namespace autoware::path_optimizer { -class PathOptimizer : public rclcpp::Node -{ -public: - explicit PathOptimizer(const rclcpp::NodeOptions & node_options); - - // NOTE: This is for the static_centerline_generator package which utilizes the following - // instance. - std::shared_ptr getMPTOptimizer() const { return mpt_optimizer_ptr_; } - - // private: -protected: // for the static_centerline_generator package - // TODO(murooka) move this node to common - class DrivingDirectionChecker + class PathOptimizer : public rclcpp::Node { public: - bool isDrivingForward(const std::vector & path_points) + explicit PathOptimizer(const rclcpp::NodeOptions & node_options); + + // NOTE: This is for the static_centerline_generator package which utilizes the following + // instance. + std::shared_ptr getMPTOptimizer() const { return mpt_optimizer_ptr_; } + + // private: + protected: // for the static_centerline_generator package + // TODO(murooka) move this node to common + class DrivingDirectionChecker { - const auto is_driving_forward = autoware::motion_utils::isDrivingForward(path_points); - is_driving_forward_ = is_driving_forward ? is_driving_forward.value() : is_driving_forward_; - return is_driving_forward_; - } + public: + bool isDrivingForward(const std::vector & path_points) + { + const auto is_driving_forward = autoware::motion_utils::isDrivingForward(path_points); + is_driving_forward_ = is_driving_forward ? is_driving_forward.value() : is_driving_forward_; + return is_driving_forward_; + } + + private: + bool is_driving_forward_{true}; + }; + DrivingDirectionChecker driving_direction_checker_{}; + + // argument variables + autoware::vehicle_info_utils::VehicleInfo vehicle_info_{}; + mutable std::shared_ptr debug_data_ptr_{nullptr}; + mutable std::shared_ptr time_keeper_{nullptr}; + + // flags for some functions + bool enable_pub_debug_marker_; + bool enable_pub_extra_debug_marker_; + bool enable_debug_info_; + bool enable_outside_drivable_area_stop_; + bool enable_skip_optimization_; + bool enable_reset_prev_optimization_; + bool use_footprint_polygon_for_outside_drivable_area_check_; + + // core algorithms + std::shared_ptr replan_checker_ptr_{nullptr}; + std::shared_ptr mpt_optimizer_ptr_{nullptr}; + + // parameters + TrajectoryParam traj_param_{}; + EgoNearestParam ego_nearest_param_{}; + + // interface publisher + rclcpp::Publisher::SharedPtr traj_pub_; + rclcpp::Publisher::SharedPtr virtual_wall_pub_; + + // interface subscriber + rclcpp::Subscription::SharedPtr path_sub_; + autoware::universe_utils::InterProcessPollingSubscriber ego_odom_sub_{ + this, "~/input/odometry"}; + + // debug publisher + rclcpp::Publisher::SharedPtr debug_extended_traj_pub_; + rclcpp::Publisher::SharedPtr debug_markers_pub_; + rclcpp::Publisher::SharedPtr debug_calculation_time_str_pub_; + rclcpp::Publisher::SharedPtr debug_calculation_time_float_pub_; + rclcpp::Publisher::SharedPtr + debug_processing_time_detail_pub_; + + // parameter callback + rcl_interfaces::msg::SetParametersResult onParam( + const std::vector & parameters); + OnSetParametersCallbackHandle::SharedPtr set_param_res_; + + // subscriber callback function + void onPath(const Path::ConstSharedPtr path_ptr); + + // reset functions + void initializePlanning(); + void resetPreviousData(); + + // main functions + bool checkInputPath(const Path & path, rclcpp::Clock clock) const; + PlannerData createPlannerData( + const Path & path, const Odometry::ConstSharedPtr ego_odom_ptr) const; + std::vector generateOptimizedTrajectory(const PlannerData & planner_data); + std::vector extendTrajectory( + const std::vector & traj_points, + const std::vector & optimized_points) const; + void publishDebugData(const Header & header) const; + + // functions in generateOptimizedTrajectory + std::vector optimizeTrajectory(const PlannerData & planner_data); + std::vector getPrevOptimizedTrajectory( + const std::vector & traj_points) const; + void applyInputVelocity( + std::vector & output_traj_points, + const std::vector & input_traj_points, + const geometry_msgs::msg::Pose & ego_pose) const; + void insertZeroVelocityOutsideDrivableArea( + const PlannerData & planner_data, std::vector & traj_points) const; + void publishVirtualWall(const geometry_msgs::msg::Pose & stop_pose) const; + void publishDebugMarkerOfOptimization(const std::vector & traj_points) const; private: - bool is_driving_forward_{true}; + double vehicle_stop_margin_outside_drivable_area_; + + std::unique_ptr logger_configure_; + + std::unique_ptr published_time_publisher_; + + autoware::universe_utils::StopWatch stop_watch_; }; - DrivingDirectionChecker driving_direction_checker_{}; - - // argument variables - autoware::vehicle_info_utils::VehicleInfo vehicle_info_{}; - mutable std::shared_ptr debug_data_ptr_{nullptr}; - mutable std::shared_ptr time_keeper_{nullptr}; - - // flags for some functions - bool enable_pub_debug_marker_; - bool enable_pub_extra_debug_marker_; - bool enable_debug_info_; - bool enable_outside_drivable_area_stop_; - bool enable_skip_optimization_; - bool enable_reset_prev_optimization_; - bool use_footprint_polygon_for_outside_drivable_area_check_; - - // core algorithms - std::shared_ptr replan_checker_ptr_{nullptr}; - std::shared_ptr mpt_optimizer_ptr_{nullptr}; - - // parameters - TrajectoryParam traj_param_{}; - EgoNearestParam ego_nearest_param_{}; - - // interface publisher - rclcpp::Publisher::SharedPtr traj_pub_; - rclcpp::Publisher::SharedPtr virtual_wall_pub_; - - // interface subscriber - rclcpp::Subscription::SharedPtr path_sub_; - autoware::universe_utils::InterProcessPollingSubscriber ego_odom_sub_{ - this, "~/input/odometry"}; - - // debug publisher - rclcpp::Publisher::SharedPtr debug_extended_traj_pub_; - rclcpp::Publisher::SharedPtr debug_markers_pub_; - rclcpp::Publisher::SharedPtr debug_calculation_time_str_pub_; - rclcpp::Publisher::SharedPtr debug_calculation_time_float_pub_; - rclcpp::Publisher::SharedPtr - debug_processing_time_detail_pub_; - - // parameter callback - rcl_interfaces::msg::SetParametersResult onParam( - const std::vector & parameters); - OnSetParametersCallbackHandle::SharedPtr set_param_res_; - - // subscriber callback function - void onPath(const Path::ConstSharedPtr path_ptr); - - // reset functions - void initializePlanning(); - void resetPreviousData(); - - // main functions - bool checkInputPath(const Path & path, rclcpp::Clock clock) const; - PlannerData createPlannerData( - const Path & path, const Odometry::ConstSharedPtr ego_odom_ptr) const; - std::vector generateOptimizedTrajectory(const PlannerData & planner_data); - std::vector extendTrajectory( - const std::vector & traj_points, - const std::vector & optimized_points) const; - void publishDebugData(const Header & header) const; - - // functions in generateOptimizedTrajectory - std::vector optimizeTrajectory(const PlannerData & planner_data); - std::vector getPrevOptimizedTrajectory( - const std::vector & traj_points) const; - void applyInputVelocity( - std::vector & output_traj_points, - const std::vector & input_traj_points, - const geometry_msgs::msg::Pose & ego_pose) const; - void insertZeroVelocityOutsideDrivableArea( - const PlannerData & planner_data, std::vector & traj_points) const; - void publishVirtualWall(const geometry_msgs::msg::Pose & stop_pose) const; - void publishDebugMarkerOfOptimization(const std::vector & traj_points) const; - -private: - double vehicle_stop_margin_outside_drivable_area_; - - std::unique_ptr logger_configure_; - - std::unique_ptr published_time_publisher_; - - autoware::universe_utils::StopWatch stop_watch_; -}; } // namespace autoware::path_optimizer #endif // AUTOWARE__PATH_OPTIMIZER__NODE_HPP_ diff --git a/planning/autoware_path_optimizer/src/node.cpp b/planning/autoware_path_optimizer/src/node.cpp index f0d101a046206..3d4ba42de0fe1 100644 --- a/planning/autoware_path_optimizer/src/node.cpp +++ b/planning/autoware_path_optimizer/src/node.cpp @@ -223,7 +223,10 @@ void PathOptimizer::resetPreviousData() void PathOptimizer::onPath(const Path::ConstSharedPtr path_ptr) { time_keeper_->start_track(__func__); +<<<<<<< HEAD +======= stop_watch_.tic(); +>>>>>>> original/main // check if input path is valid if (!checkInputPath(*path_ptr, *get_clock())) { @@ -271,7 +274,16 @@ void PathOptimizer::onPath(const Path::ConstSharedPtr path_ptr) // publish calculation_time // NOTE: This function must be called after measuring onPath calculation time +<<<<<<< HEAD + /* + const auto calculation_time_msg = createStringStamped(now(), time_keeper_ptr_->getLog()); + debug_calculation_time_str_pub_->publish(calculation_time_msg); + debug_calculation_time_float_pub_->publish( + createFloat64Stamped(now(), time_keeper_ptr_->getAccumulatedTime())); + */ +======= debug_calculation_time_float_pub_->publish(createFloat64Stamped(now(), stop_watch_.toc())); +>>>>>>> original/main const auto output_traj_msg = autoware::motion_utils::convertToTrajectory(full_traj_points, path_ptr->header); diff --git a/planning/autoware_rtc_interface/src/rtc_interface.cpp b/planning/autoware_rtc_interface/src/rtc_interface.cpp index 5ccbbf0e3c6e5..0ee51a3e800f0 100644 --- a/planning/autoware_rtc_interface/src/rtc_interface.cpp +++ b/planning/autoware_rtc_interface/src/rtc_interface.cpp @@ -400,7 +400,11 @@ bool RTCInterface::isTerminated(const UUID & uuid) const std::lock_guard lock(mutex_); const auto itr = std::find_if( registered_status_.statuses.begin(), registered_status_.statuses.end(), +<<<<<<< HEAD + [uuid](auto & s) { return s.uuid == uuid; }); +======= [uuid](const auto & s) { return s.uuid == uuid; }); +>>>>>>> original/main if (itr != registered_status_.statuses.end()) { return itr->state.type == State::SUCCEEDED || itr->state.type == State::FAILED; diff --git a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/utils.hpp b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/utils.hpp index 703d882794ee4..fc284d53c0efd 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/utils.hpp +++ b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/utils.hpp @@ -178,10 +178,17 @@ bool isParkedObject( const ExtendedPredictedObject & object, const double buffer_to_bound, const double ratio_threshold); +<<<<<<< HEAD +bool passParkedObject( + const CommonDataPtr & common_data_ptr, const LaneChangePath & lane_change_path, + const std::vector & objects, const double minimum_lane_change_length, + const bool is_goal_in_route, CollisionCheckDebugMap & object_debug); +======= bool passed_parked_objects( const CommonDataPtr & common_data_ptr, const LaneChangePath & lane_change_path, const std::vector & objects, const double minimum_lane_change_length, CollisionCheckDebugMap & object_debug); +>>>>>>> original/main std::optional getLeadingStaticObjectIdx( const RouteHandler & route_handler, const LaneChangePath & lane_change_path, diff --git a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/scene.cpp b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/scene.cpp index d00e47ce68097..e42b5adbb9ca5 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/scene.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/scene.cpp @@ -1628,9 +1628,16 @@ bool NormalLaneChange::getLaneChangePaths( candidate_paths->push_back(*candidate_path); if ( +<<<<<<< HEAD + !is_stuck && + utils::lane_change::passParkedObject( + common_data_ptr_, *candidate_path, filtered_objects.target_lane, lane_change_buffer, + is_goal_in_route, lane_change_debug_.collision_check_objects)) { +======= !is_stuck && !utils::lane_change::passed_parked_objects( common_data_ptr_, *candidate_path, filtered_objects.target_lane, lane_change_buffer, lane_change_debug_.collision_check_objects)) { +>>>>>>> original/main debug_print_lat( "Reject: parking vehicle exists in the target lane, and the ego is not in stuck. Skip " "lane change."); diff --git a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/utils/utils.cpp b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/utils/utils.cpp index fc8d1d31f9d27..930b6edd1cc8f 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/utils/utils.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/utils/utils.cpp @@ -968,10 +968,17 @@ bool isParkedObject( return clamped_ratio > ratio_threshold; } +<<<<<<< HEAD +bool passParkedObject( + const CommonDataPtr & common_data_ptr, const LaneChangePath & lane_change_path, + const std::vector & objects, const double minimum_lane_change_length, + const bool is_goal_in_route, CollisionCheckDebugMap & object_debug) +======= bool passed_parked_objects( const CommonDataPtr & common_data_ptr, const LaneChangePath & lane_change_path, const std::vector & objects, const double minimum_lane_change_length, CollisionCheckDebugMap & object_debug) +>>>>>>> original/main { const auto route_handler = *common_data_ptr->route_handler_ptr; const auto lane_change_parameters = *common_data_ptr->lc_param_ptr; @@ -979,6 +986,10 @@ bool passed_parked_objects( lane_change_parameters.object_check_min_road_shoulder_width; const auto & object_shiftable_ratio_threshold = lane_change_parameters.object_shiftable_ratio_threshold; +<<<<<<< HEAD + const auto & path = lane_change_path.path; +======= +>>>>>>> original/main const auto & current_lanes = common_data_ptr->lanes_ptr->current; const auto current_lane_path = route_handler.getCenterLinePath(current_lanes, 0.0, std::numeric_limits::max()); diff --git a/sensing/autoware_pointcloud_preprocessor/README.md b/sensing/autoware_pointcloud_preprocessor/README.md index 4424259a52daa..d2ebb461674ca 100644 --- a/sensing/autoware_pointcloud_preprocessor/README.md +++ b/sensing/autoware_pointcloud_preprocessor/README.md @@ -41,18 +41,9 @@ Detail description of each filter's algorithm is in the following links. | ----------------- | ------------------------------- | --------------- | | `~/output/points` | `sensor_msgs::msg::PointCloud2` | filtered points | -## Parameters - -### Node Parameters - -| Name | Type | Default Value | Description | -| ------------------ | ------ | ------------- | ------------------------------------- | -| `input_frame` | string | " " | input frame id | -| `output_frame` | string | " " | output frame id | -| `max_queue_size` | int | 5 | max queue size of input/output topics | -| `use_indices` | bool | false | flag to use pointcloud indices | -| `latched_indices` | bool | false | flag to latch pointcloud indices | -| `approximate_sync` | bool | false | flag to use approximate sync option | +## Core Parameters + +{{ json_to_markdown("sensing/pointcloud_preprocessor/schema/filter.schema.json") }} ## Assumptions / Known limits diff --git a/sensing/autoware_pointcloud_preprocessor/config/approximate_downsample_filter.param.yaml b/sensing/autoware_pointcloud_preprocessor/config/approximate_downsample_filter.param.yaml new file mode 100644 index 0000000000000..239e47f09632b --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/config/approximate_downsample_filter.param.yaml @@ -0,0 +1,5 @@ +/**: + ros__parameters: + voxel_size_x: 0.3 + voxel_size_y: 0.3 + voxel_size_z: 0.1 diff --git a/sensing/autoware_pointcloud_preprocessor/config/blockage_diagnostics_param_file.yaml b/sensing/autoware_pointcloud_preprocessor/config/blockage_diagnostics.param.yaml similarity index 79% rename from sensing/autoware_pointcloud_preprocessor/config/blockage_diagnostics_param_file.yaml rename to sensing/autoware_pointcloud_preprocessor/config/blockage_diagnostics.param.yaml index 13ddd8a7c7590..75503a3caf159 100644 --- a/sensing/autoware_pointcloud_preprocessor/config/blockage_diagnostics_param_file.yaml +++ b/sensing/autoware_pointcloud_preprocessor/config/blockage_diagnostics.param.yaml @@ -14,3 +14,7 @@ max_distance_range: 200.0 horizontal_resolution: 0.4 blockage_kernel: 10 + angle_range: [0.0, 360.0] + vertical_bins: 40 + is_channel_order_top2down: true + horizontal_ring_id: 18 diff --git a/sensing/autoware_pointcloud_preprocessor/config/concatenate_and_time_sync.param.yaml b/sensing/autoware_pointcloud_preprocessor/config/concatenate_and_time_sync.param.yaml new file mode 100644 index 0000000000000..a3a6d4c585d99 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/config/concatenate_and_time_sync.param.yaml @@ -0,0 +1,11 @@ +/**: + ros__parameters: + input_twist_topic_type: "twist" + output_frame: "" + input_topics: [""] + max_queue_size: 5 + timeout_sec: 0.1 + input_offset: {} + publish_synchronized_pointcloud: true + keep_input_frame_in_synchronized_pointcloud: true + synchronized_pointcloud_postfix: "pointcloud" diff --git a/sensing/autoware_pointcloud_preprocessor/config/concatenate_pointclouds.param.yaml b/sensing/autoware_pointcloud_preprocessor/config/concatenate_pointclouds.param.yaml new file mode 100644 index 0000000000000..318d37a38d132 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/config/concatenate_pointclouds.param.yaml @@ -0,0 +1,7 @@ +/**: + ros__parameters: + output_frame: "" + input_topics: [""] + max_queue_size: 5 + timeout_sec: 0.033 + input_offset: {} diff --git a/sensing/autoware_pointcloud_preprocessor/config/crop_box_filter.param.yaml b/sensing/autoware_pointcloud_preprocessor/config/crop_box_filter.param.yaml new file mode 100644 index 0000000000000..4ec29e8f66137 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/config/crop_box_filter.param.yaml @@ -0,0 +1,9 @@ +/**: + ros__parameters: + min_x: -1.0 + min_y: -1.0 + min_z: -1.0 + max_x: 1.0 + max_y: 1.0 + max_z: 1.0 + negative: false diff --git a/sensing/autoware_pointcloud_preprocessor/config/dual_return_outlier_filter.param.yaml b/sensing/autoware_pointcloud_preprocessor/config/dual_return_outlier_filter.param.yaml new file mode 100644 index 0000000000000..5454176d7f319 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/config/dual_return_outlier_filter.param.yaml @@ -0,0 +1,19 @@ +/**: + ros__parameters: + x_max: 18.0 + x_min: -12.0 + y_max: 2.0 + y_min: -2.0 + z_max: 10.0 + z_min: 0.0 + min_azimuth_deg: 135.0 + max_azimuth_deg: 225.0 + max_distance: 12.0 + vertical_bins: 128 + max_azimuth_diff: 50.0 + weak_first_distance_ratio: 1.004 + general_distance_ratio: 1.03 + weak_first_local_noise_threshold: 2 + roi_mode: "Fixed_xyz_ROI" + visibility_error_threshold: 0.5 + visibility_warn_threshold: 0.7 diff --git a/sensing/autoware_pointcloud_preprocessor/config/filter.param.yaml b/sensing/autoware_pointcloud_preprocessor/config/filter.param.yaml new file mode 100644 index 0000000000000..abd396892239e --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/config/filter.param.yaml @@ -0,0 +1,8 @@ +/**: + ros__parameters: + input_frame: "" + output_frame: "" + max_queue_size: 5 + use_indices: false + latched_indices: false + approximate_sync: false diff --git a/sensing/autoware_pointcloud_preprocessor/config/lanelet2_map_filter.param.yaml b/sensing/autoware_pointcloud_preprocessor/config/lanelet2_map_filter.param.yaml new file mode 100644 index 0000000000000..2aae4c5e886ea --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/config/lanelet2_map_filter.param.yaml @@ -0,0 +1,4 @@ +/**: + ros__parameters: + voxel_size_x: 0.04 + voxel_size_y: 0.04 diff --git a/sensing/autoware_pointcloud_preprocessor/config/passthrough_filter_unit16.param.yaml b/sensing/autoware_pointcloud_preprocessor/config/passthrough_filter_unit16.param.yaml new file mode 100644 index 0000000000000..86d0c02b2c1ae --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/config/passthrough_filter_unit16.param.yaml @@ -0,0 +1,7 @@ +/**: + ros__parameters: + filter_limit_min: 0 + filter_limit_max: 127 + filter_field_name: "ring" + keep_organized: false + filter_limit_negative: false diff --git a/sensing/autoware_pointcloud_preprocessor/config/pickup_based_voxel_grid_downsample_filter.param.yaml b/sensing/autoware_pointcloud_preprocessor/config/pickup_based_voxel_grid_downsample_filter.param.yaml new file mode 100644 index 0000000000000..3a996929e91ef --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/config/pickup_based_voxel_grid_downsample_filter.param.yaml @@ -0,0 +1,5 @@ +/**: + ros__parameters: + voxel_size_x: 1.0 + voxel_size_y: 1.0 + voxel_size_z: 1.0 diff --git a/sensing/autoware_pointcloud_preprocessor/config/pointcloud_accumulator.param.yaml b/sensing/autoware_pointcloud_preprocessor/config/pointcloud_accumulator.param.yaml new file mode 100644 index 0000000000000..37ce94696db42 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/config/pointcloud_accumulator.param.yaml @@ -0,0 +1,4 @@ +/**: + ros__parameters: + accumulation_time_sec: 2.0 + pointcloud_buffer_size: 50 diff --git a/sensing/autoware_pointcloud_preprocessor/config/radius_search_2d_outlier_filter.param.yaml b/sensing/autoware_pointcloud_preprocessor/config/radius_search_2d_outlier_filter.param.yaml new file mode 100644 index 0000000000000..8d2d32584c86d --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/config/radius_search_2d_outlier_filter.param.yaml @@ -0,0 +1,4 @@ +/**: + ros__parameters: + min_neighbors: 5 + search_radius: 0.2 diff --git a/sensing/autoware_pointcloud_preprocessor/config/random_downsample_filter.param.yaml b/sensing/autoware_pointcloud_preprocessor/config/random_downsample_filter.param.yaml new file mode 100644 index 0000000000000..53be849e0af22 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/config/random_downsample_filter.param.yaml @@ -0,0 +1,3 @@ +/**: + ros__parameters: + sample_num: 1500 diff --git a/sensing/autoware_pointcloud_preprocessor/config/ring_outlier_filter.param.yaml b/sensing/autoware_pointcloud_preprocessor/config/ring_outlier_filter.param.yaml new file mode 100644 index 0000000000000..76bf68958f504 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/config/ring_outlier_filter.param.yaml @@ -0,0 +1,14 @@ +/**: + ros__parameters: + distance_ratio: 1.03 + object_length_threshold: 0.1 + num_points_threshold: 4 + max_rings_num: 128 + max_points_num_per_ring: 4000 + publish_outlier_pointcloud: false + min_azimuth_deg: 0.0 + max_azimuth_deg: 360.0 + max_distance: 12.0 + vertical_bins: 128 + horizontal_bins: 36 + noise_threshold: 2 diff --git a/sensing/autoware_pointcloud_preprocessor/config/time_synchronizer.param.yaml b/sensing/autoware_pointcloud_preprocessor/config/time_synchronizer.param.yaml new file mode 100644 index 0000000000000..4b3f296738020 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/config/time_synchronizer.param.yaml @@ -0,0 +1,10 @@ +/**: + ros__parameters: + input_twist_topic_type: "twist" + output_frame: "" + keep_input_frame_in_synchronized_pointcloud: false + input_topics: [""] + synchronized_pointcloud_postfix: "pointcloud" + max_queue_size: 5 + timeout_sec: 0.1 + input_offset: {} diff --git a/sensing/autoware_pointcloud_preprocessor/config/vector_map_inside_area_filter.param.yaml b/sensing/autoware_pointcloud_preprocessor/config/vector_map_inside_area_filter.param.yaml new file mode 100644 index 0000000000000..e7df8c9641a8b --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/config/vector_map_inside_area_filter.param.yaml @@ -0,0 +1,5 @@ +/**: + ros__parameters: + polygon_type: "no_obstacle_segmentation_area" + use_z_filter: false + z_threshold: 0.0 diff --git a/sensing/autoware_pointcloud_preprocessor/config/voxel_grid_downsample_filter.param.yaml b/sensing/autoware_pointcloud_preprocessor/config/voxel_grid_downsample_filter.param.yaml new file mode 100644 index 0000000000000..239e47f09632b --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/config/voxel_grid_downsample_filter.param.yaml @@ -0,0 +1,5 @@ +/**: + ros__parameters: + voxel_size_x: 0.3 + voxel_size_y: 0.3 + voxel_size_z: 0.1 diff --git a/sensing/autoware_pointcloud_preprocessor/config/voxel_grid_outlier_filter.param.yaml b/sensing/autoware_pointcloud_preprocessor/config/voxel_grid_outlier_filter.param.yaml new file mode 100644 index 0000000000000..2ff4c64d5f835 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/config/voxel_grid_outlier_filter.param.yaml @@ -0,0 +1,6 @@ +/**: + ros__parameters: + voxel_size_x: 0.3 + voxel_size_y: 0.3 + voxel_size_z: 0.1 + voxel_points_threshold: 2 diff --git a/sensing/autoware_pointcloud_preprocessor/docs/blockage_diag.md b/sensing/autoware_pointcloud_preprocessor/docs/blockage_diag.md index b6f858ada3a98..a7e9ec3114882 100644 --- a/sensing/autoware_pointcloud_preprocessor/docs/blockage_diag.md +++ b/sensing/autoware_pointcloud_preprocessor/docs/blockage_diag.md @@ -51,24 +51,7 @@ This implementation inherits `autoware::pointcloud_preprocessor::Filter` class, ## Parameters -| Name | Type | Description | -| ----------------------------- | ------ | ----------------------------------------------------------------------------------------------------------------------------- | -| `blockage_ratio_threshold` | float | The threshold of blockage area ratio.If the blockage value exceeds this threshold, the diagnostic state will be set to ERROR. | -| `blockage_count_threshold` | float | The threshold of number continuous blockage frames | -| `horizontal_ring_id` | int | The id of horizontal ring of the LiDAR | -| `angle_range` | vector | The effective range of LiDAR | -| `vertical_bins` | int | The LiDAR channel number | -| `is_channel_order_top2down` | bool | If the lidar channels are indexed from top to down | -| `blockage_buffering_frames` | int | The number of buffering about blockage detection [range:1-200] | -| `blockage_buffering_interval` | int | The interval of buffering about blockage detection | -| `dust_ratio_threshold` | float | The threshold of dusty area ratio | -| `dust_count_threshold` | int | The threshold of number continuous frames include dusty area | -| `blockage_kernel` | int | The kernel size of morphology processing the detected blockage area | -| `dust_kernel_size` | int | The kernel size of morphology processing in dusty area detection | -| `dust_buffering_frames` | int | The number of buffering about dusty area detection [range:1-200] | -| `dust_buffering_interval` | int | The interval of buffering about dusty area detection | -| `max_distance_range` | double | Maximum view range for the LiDAR | -| `horizontal_resolution` | double | The horizontal resolution of depth map image [deg/pixel] | +{{ json_to_markdown("sensing/autoware_pointcloud_preprocessor/schema/blockage_diag.schema.json") }} ## Assumptions / Known limits diff --git a/sensing/autoware_pointcloud_preprocessor/docs/crop-box-filter.md b/sensing/autoware_pointcloud_preprocessor/docs/crop-box-filter.md index 4539a85e26a1d..22778d79839bb 100644 --- a/sensing/autoware_pointcloud_preprocessor/docs/crop-box-filter.md +++ b/sensing/autoware_pointcloud_preprocessor/docs/crop-box-filter.md @@ -20,14 +20,7 @@ This implementation inherit `autoware::pointcloud_preprocessor::Filter` class, p ### Core Parameters -| Name | Type | Default Value | Description | -| ------- | ------ | ------------- | ----------------------------------------- | -| `min_x` | double | -1.0 | x-coordinate minimum value for crop range | -| `max_x` | double | 1.0 | x-coordinate maximum value for crop range | -| `min_y` | double | -1.0 | y-coordinate minimum value for crop range | -| `max_y` | double | 1.0 | y-coordinate maximum value for crop range | -| `min_z` | double | -1.0 | z-coordinate minimum value for crop range | -| `max_z` | double | 1.0 | z-coordinate maximum value for crop range | +{{ json_to_markdown("sensing/autoware_pointcloud_preprocessor/schema/crop_box_filter.schema.json") }} ## Assumptions / Known limits diff --git a/sensing/autoware_pointcloud_preprocessor/docs/downsample-filter.md b/sensing/autoware_pointcloud_preprocessor/docs/downsample-filter.md index ac721171cb014..6c1204f013987 100644 --- a/sensing/autoware_pointcloud_preprocessor/docs/downsample-filter.md +++ b/sensing/autoware_pointcloud_preprocessor/docs/downsample-filter.md @@ -36,33 +36,19 @@ These implementations inherit `autoware::pointcloud_preprocessor::Filter` class, #### Approximate Downsample Filter -| Name | Type | Default Value | Description | -| -------------- | ------ | ------------- | ---------------- | -| `voxel_size_x` | double | 0.3 | voxel size x [m] | -| `voxel_size_y` | double | 0.3 | voxel size y [m] | -| `voxel_size_z` | double | 0.1 | voxel size z [m] | +{{ json_to_markdown("sensing/autoware_pointcloud_preprocessor/schema/approximate_downsample_filter.schema.json") }} ### Random Downsample Filter -| Name | Type | Default Value | Description | -| ------------ | ---- | ------------- | ------------------------------- | -| `sample_num` | int | 1500 | number of indices to be sampled | +{{ json_to_markdown("sensing/autoware_pointcloud_preprocessor/schema/random_downsample_filter.schema.json") }} ### Voxel Grid Downsample Filter -| Name | Type | Default Value | Description | -| -------------- | ------ | ------------- | ---------------- | -| `voxel_size_x` | double | 0.3 | voxel size x [m] | -| `voxel_size_y` | double | 0.3 | voxel size y [m] | -| `voxel_size_z` | double | 0.1 | voxel size z [m] | +{{ json_to_markdown("sensing/autoware_pointcloud_preprocessor/schema/voxel_grid_downsample_filter.schema.json") }} ### Pickup Based Voxel Grid Downsample Filter -| Name | Type | Default Value | Description | -| -------------- | ------ | ------------- | ---------------- | -| `voxel_size_x` | double | 1.0 | voxel size x [m] | -| `voxel_size_y` | double | 1.0 | voxel size y [m] | -| `voxel_size_z` | double | 1.0 | voxel size z [m] | +{{ json_to_markdown("sensing/autoware_pointcloud_preprocessor/schema/pickup_based_voxel_grid_downsample_filter.schema.json") }} ## Assumptions / Known limits diff --git a/sensing/autoware_pointcloud_preprocessor/docs/dual-return-outlier-filter.md b/sensing/autoware_pointcloud_preprocessor/docs/dual-return-outlier-filter.md index 6c9a7ed14c2eb..2a7d28ade0559 100644 --- a/sensing/autoware_pointcloud_preprocessor/docs/dual-return-outlier-filter.md +++ b/sensing/autoware_pointcloud_preprocessor/docs/dual-return-outlier-filter.md @@ -50,25 +50,7 @@ This implementation inherits `autoware::pointcloud_preprocessor::Filter` class, ### Core Parameters -| Name | Type | Description | -| ---------------------------------- | ------ | ------------------------------------------------------------------------------------------------------------------------- | -| `vertical_bins` | int | The number of vertical bin for visibility histogram | -| `max_azimuth_diff` | float | Threshold for ring_outlier_filter | -| `weak_first_distance_ratio` | double | Threshold for ring_outlier_filter | -| `general_distance_ratio` | double | Threshold for ring_outlier_filter | -| `weak_first_local_noise_threshold` | int | The parameter for determining whether it is noise | -| `visibility_error_threshold` | float | When the percentage of white pixels in the binary histogram falls below this parameter the diagnostic status becomes ERR | -| `visibility_warn_threshold` | float | When the percentage of white pixels in the binary histogram falls below this parameter the diagnostic status becomes WARN | -| `roi_mode` | string | The name of ROI mode for switching | -| `min_azimuth_deg` | float | The left limit of azimuth for `Fixed_azimuth_ROI` mode | -| `max_azimuth_deg` | float | The right limit of azimuth for `Fixed_azimuth_ROI` mode | -| `max_distance` | float | The limit distance for for `Fixed_azimuth_ROI` mode | -| `x_max` | float | Maximum of x for `Fixed_xyz_ROI` mode | -| `x_min` | float | Minimum of x for `Fixed_xyz_ROI` mode | -| `y_max` | float | Maximum of y for `Fixed_xyz_ROI` mode | -| `y_min` | float | Minimum of y for `Fixed_xyz_ROI` mode | -| `z_max` | float | Maximum of z for `Fixed_xyz_ROI` mode | -| `z_min` | float | Minimum of z for `Fixed_xyz_ROI` mode | +{{ json_to_markdown("sensing/autoware_pointcloud_preprocessor/schema/dual_return_outlier_filter.schema.json") }} ## Assumptions / Known limits diff --git a/sensing/autoware_pointcloud_preprocessor/docs/passthrough-filter.md b/sensing/autoware_pointcloud_preprocessor/docs/passthrough-filter.md index 4d28f08da28cc..1b54ecad8a1be 100644 --- a/sensing/autoware_pointcloud_preprocessor/docs/passthrough-filter.md +++ b/sensing/autoware_pointcloud_preprocessor/docs/passthrough-filter.md @@ -25,13 +25,7 @@ The `passthrough_filter` is a node that removes points on the outside of a range ### Core Parameters -| Name | Type | Default Value | Description | -| ----------------------- | ------ | ------------- | ------------------------------------------------------ | -| `filter_limit_min` | int | 0 | minimum allowed field value | -| `filter_limit_max` | int | 127 | maximum allowed field value | -| `filter_field_name` | string | "ring" | filtering field name | -| `keep_organized` | bool | false | flag to keep indices structure | -| `filter_limit_negative` | bool | false | flag to return whether the data is inside limit or not | +{{ json_to_markdown("sensing/autoware_pointcloud_preprocessor/schema/passthrough_filter.schema.json") }} ## Assumptions / Known limits diff --git a/sensing/autoware_pointcloud_preprocessor/docs/pointcloud-accumulator.md b/sensing/autoware_pointcloud_preprocessor/docs/pointcloud-accumulator.md index d9fa56ca9c995..7925cecba75d0 100644 --- a/sensing/autoware_pointcloud_preprocessor/docs/pointcloud-accumulator.md +++ b/sensing/autoware_pointcloud_preprocessor/docs/pointcloud-accumulator.md @@ -24,10 +24,7 @@ The `pointcloud_accumulator` is a node that accumulates pointclouds for a given ### Core Parameters -| Name | Type | Default Value | Description | -| ------------------------ | ------ | ------------- | ----------------------- | -| `accumulation_time_sec` | double | 2.0 | accumulation period [s] | -| `pointcloud_buffer_size` | int | 50 | buffer size | +{{ json_to_markdown("sensing/autoware_pointcloud_preprocessor/schema/pointcloud_accumulator.schema.json") }} ## Assumptions / Known limits diff --git a/sensing/autoware_pointcloud_preprocessor/docs/radius-search-2d-outlier-filter.md b/sensing/autoware_pointcloud_preprocessor/docs/radius-search-2d-outlier-filter.md index d0d621b586da6..17696017a1903 100644 --- a/sensing/autoware_pointcloud_preprocessor/docs/radius-search-2d-outlier-filter.md +++ b/sensing/autoware_pointcloud_preprocessor/docs/radius-search-2d-outlier-filter.md @@ -24,10 +24,7 @@ This implementation inherits `autoware::pointcloud_preprocessor::Filter` class, ### Core Parameters -| Name | Type | Description | -| --------------- | ------ | ------------------------------------------------------------------------------------------------------------------------ | -| `min_neighbors` | int | If points in the circle centered on reference point is less than `min_neighbors`, a reference point is judged as outlier | -| `search_radius` | double | Searching number of points included in `search_radius` | +{{ json_to_markdown("sensing/autoware_pointcloud_preprocessor/schema/radius_search_2d_outlier_filter.schema.json") }} ## Assumptions / Known limits diff --git a/sensing/autoware_pointcloud_preprocessor/docs/ring-outlier-filter.md b/sensing/autoware_pointcloud_preprocessor/docs/ring-outlier-filter.md index 7f2efe90a674f..2955cb47a0f81 100644 --- a/sensing/autoware_pointcloud_preprocessor/docs/ring-outlier-filter.md +++ b/sensing/autoware_pointcloud_preprocessor/docs/ring-outlier-filter.md @@ -56,20 +56,7 @@ This implementation inherits `autoware::pointcloud_preprocessor::Filter` class, ### Core Parameters -| Name | Type | Default Value | Description | -| ---------------------------- | ------- | ------------- | ----------------------------------------------------------------------------------------------------------------------------- | -| `distance_ratio` | double | 1.03 | | -| `object_length_threshold` | double | 0.1 | | -| `num_points_threshold` | int | 4 | | -| `max_rings_num` | uint_16 | 128 | | -| `max_points_num_per_ring` | size_t | 4000 | Set this value large enough such that `HFoV / resolution < max_points_num_per_ring` | -| `publish_outlier_pointcloud` | bool | false | Flag to publish outlier pointcloud and visibility score. Due to performance concerns, please set to false during experiments. | -| `min_azimuth_deg` | float | 0.0 | The left limit of azimuth for visibility score calculation | -| `max_azimuth_deg` | float | 360.0 | The right limit of azimuth for visibility score calculation | -| `max_distance` | float | 12.0 | The limit distance for visibility score calculation | -| `vertical_bins` | int | 128 | The number of vertical bin for visibility histogram | -| `horizontal_bins` | int | 36 | The number of horizontal bin for visibility histogram | -| `noise_threshold` | int | 2 | The threshold value for distinguishing noise from valid points in the frequency image | +{{ json_to_markdown("sensing/autoware_pointcloud_preprocessor/schema/ring_outlier_filter.schema.json") }} ## Assumptions / Known limits diff --git a/sensing/autoware_pointcloud_preprocessor/docs/vector-map-filter.md b/sensing/autoware_pointcloud_preprocessor/docs/vector-map-filter.md index c38a4c719fcf3..61be9921bbb5c 100644 --- a/sensing/autoware_pointcloud_preprocessor/docs/vector-map-filter.md +++ b/sensing/autoware_pointcloud_preprocessor/docs/vector-map-filter.md @@ -25,10 +25,7 @@ The `vector_map_filter` is a node that removes points on the outside of lane by ### Core Parameters -| Name | Type | Default Value | Description | -| -------------- | ------ | ------------- | ----------- | -| `voxel_size_x` | double | 0.04 | voxel size | -| `voxel_size_y` | double | 0.04 | voxel size | +{{ json_to_markdown("sensing/autoware_pointcloud_preprocessor/schema/lanelet2_map_filter.schema.json") }} ## Assumptions / Known limits diff --git a/sensing/autoware_pointcloud_preprocessor/docs/vector-map-inside-area-filter.md b/sensing/autoware_pointcloud_preprocessor/docs/vector-map-inside-area-filter.md index 1b6f86a7e4f27..fae421d44a29e 100644 --- a/sensing/autoware_pointcloud_preprocessor/docs/vector-map-inside-area-filter.md +++ b/sensing/autoware_pointcloud_preprocessor/docs/vector-map-inside-area-filter.md @@ -33,10 +33,6 @@ This implementation inherits `autoware::pointcloud_preprocessor::Filter` class, ### Core Parameters -| Name | Type | Description | -| -------------- | ------ | --------------------------- | -| `polygon_type` | string | polygon type to be filtered | -| `use_z` | bool | use z value for filtering | -| `z_threshold` | float | z threshold for filtering | +{{ json_to_markdown("sensing/autoware_pointcloud_preprocessor/schema/vector_map_inside_area_filter.schema.json") }} ## Assumptions / Known limits diff --git a/sensing/autoware_pointcloud_preprocessor/docs/voxel-grid-outlier-filter.md b/sensing/autoware_pointcloud_preprocessor/docs/voxel-grid-outlier-filter.md index d99b54ef09a73..8d8cb0cde26df 100644 --- a/sensing/autoware_pointcloud_preprocessor/docs/voxel-grid-outlier-filter.md +++ b/sensing/autoware_pointcloud_preprocessor/docs/voxel-grid-outlier-filter.md @@ -23,12 +23,7 @@ This implementation inherits `autoware::pointcloud_preprocessor::Filter` class, ### Core Parameters -| Name | Type | Default Value | Description | -| ------------------------ | ------ | ------------- | ------------------------------------------ | -| `voxel_size_x` | double | 0.3 | the voxel size along x-axis [m] | -| `voxel_size_y` | double | 0.3 | the voxel size along y-axis [m] | -| `voxel_size_z` | double | 0.1 | the voxel size along z-axis [m] | -| `voxel_points_threshold` | int | 2 | the minimum number of points in each voxel | +{{ json_to_markdown("sensing/autoware_pointcloud_preprocessor/schema/voxel_grid_outlier_filter.schema.json") }} ## Assumptions / Known limits diff --git a/sensing/autoware_pointcloud_preprocessor/include/autoware/pointcloud_preprocessor/distortion_corrector/distortion_corrector_node.hpp b/sensing/autoware_pointcloud_preprocessor/include/autoware/pointcloud_preprocessor/distortion_corrector/distortion_corrector_node.hpp index 0d8c9436bda4b..e772bed6ecea4 100644 --- a/sensing/autoware_pointcloud_preprocessor/include/autoware/pointcloud_preprocessor/distortion_corrector/distortion_corrector_node.hpp +++ b/sensing/autoware_pointcloud_preprocessor/include/autoware/pointcloud_preprocessor/distortion_corrector/distortion_corrector_node.hpp @@ -12,10 +12,22 @@ // See the License for the specific language governing permissions and // limitations under the License. +<<<<<<<< + HEAD : sensing / autoware_pointcloud_preprocessor / include / pointcloud_preprocessor / + distortion_corrector / distortion_corrector_node.hpp +#ifndef AUTOWARE__POINTCLOUD_PREPROCESSOR__DISTORTION_CORRECTOR__DISTORTION_CORRECTOR_NODE_HPP_ +#define AUTOWARE__POINTCLOUD_PREPROCESSOR__DISTORTION_CORRECTOR__DISTORTION_CORRECTOR_NODE_HPP_ + +#include "pointcloud_preprocessor/distortion_corrector/distortion_corrector.hpp" + == == == == #ifndef AUTOWARE__POINTCLOUD_PREPROCESSOR__DISTORTION_CORRECTOR__DISTORTION_CORRECTOR_NODE_HPP_ #define AUTOWARE__POINTCLOUD_PREPROCESSOR__DISTORTION_CORRECTOR__DISTORTION_CORRECTOR_NODE_HPP_ #include "autoware/pointcloud_preprocessor/distortion_corrector/distortion_corrector.hpp" + >>>>>>>> original / + main : sensing / autoware_pointcloud_preprocessor / include / autoware / pointcloud_preprocessor / + distortion_corrector / + distortion_corrector_node.hpp #include #include @@ -29,37 +41,45 @@ #include #include -namespace autoware::pointcloud_preprocessor + namespace autoware::pointcloud_preprocessor { -using rcl_interfaces::msg::SetParametersResult; -using sensor_msgs::msg::PointCloud2; + using rcl_interfaces::msg::SetParametersResult; + using sensor_msgs::msg::PointCloud2; -class DistortionCorrectorComponent : public rclcpp::Node -{ -public: - explicit DistortionCorrectorComponent(const rclcpp::NodeOptions & options); + class DistortionCorrectorComponent : public rclcpp::Node + { + public: + explicit DistortionCorrectorComponent(const rclcpp::NodeOptions & options); -private: - rclcpp::Subscription::SharedPtr twist_sub_; - rclcpp::Subscription::SharedPtr imu_sub_; - rclcpp::Subscription::SharedPtr pointcloud_sub_; + private: + rclcpp::Subscription::SharedPtr twist_sub_; + rclcpp::Subscription::SharedPtr imu_sub_; + rclcpp::Subscription::SharedPtr pointcloud_sub_; - rclcpp::Publisher::SharedPtr undistorted_pointcloud_pub_; + rclcpp::Publisher::SharedPtr undistorted_pointcloud_pub_; - std::unique_ptr> stop_watch_ptr_; - std::unique_ptr debug_publisher_; + std::unique_ptr> stop_watch_ptr_; + std::unique_ptr debug_publisher_; - std::string base_frame_; - bool use_imu_; - bool use_3d_distortion_correction_; + std::string base_frame_; + bool use_imu_; + bool use_3d_distortion_correction_; - std::unique_ptr distortion_corrector_; + std::unique_ptr distortion_corrector_; - void onPointCloud(PointCloud2::UniquePtr points_msg); - void onTwist(const geometry_msgs::msg::TwistWithCovarianceStamped::ConstSharedPtr twist_msg); - void onImu(const sensor_msgs::msg::Imu::ConstSharedPtr imu_msg); -}; + void onPointCloud(PointCloud2::UniquePtr points_msg); + void onTwist(const geometry_msgs::msg::TwistWithCovarianceStamped::ConstSharedPtr twist_msg); + void onImu(const sensor_msgs::msg::Imu::ConstSharedPtr imu_msg); + }; } // namespace autoware::pointcloud_preprocessor +<<<<<<<< + HEAD : sensing / autoware_pointcloud_preprocessor / include / pointcloud_preprocessor / + distortion_corrector / distortion_corrector_node.hpp +#endif // POINTCLOUD_PREPROCESSOR__DISTORTION_CORRECTOR__DISTORTION_CORRECTOR_NODE_HPP_ + == == == == #endif // AUTOWARE__POINTCLOUD_PREPROCESSOR__DISTORTION_CORRECTOR__DISTORTION_CORRECTOR_NODE_HPP_ + >>>>>>>> original / + main : sensing / autoware_pointcloud_preprocessor / include / autoware / pointcloud_preprocessor / + distortion_corrector / distortion_corrector_node.hpp diff --git a/sensing/autoware_pointcloud_preprocessor/include/autoware/pointcloud_preprocessor/polygon_remover/polygon_remover.hpp b/sensing/autoware_pointcloud_preprocessor/include/autoware/pointcloud_preprocessor/polygon_remover/polygon_remover.hpp index 3054a4454fb00..a2451a6d29600 100644 --- a/sensing/autoware_pointcloud_preprocessor/include/autoware/pointcloud_preprocessor/polygon_remover/polygon_remover.hpp +++ b/sensing/autoware_pointcloud_preprocessor/include/autoware/pointcloud_preprocessor/polygon_remover/polygon_remover.hpp @@ -15,8 +15,18 @@ #ifndef AUTOWARE__POINTCLOUD_PREPROCESSOR__POLYGON_REMOVER__POLYGON_REMOVER_HPP_ #define AUTOWARE__POINTCLOUD_PREPROCESSOR__POLYGON_REMOVER__POLYGON_REMOVER_HPP_ +<<<<<<<< + HEAD : sensing / autoware_pointcloud_preprocessor / include / pointcloud_preprocessor / + polygon_remover / polygon_remover.hpp +#include "pointcloud_preprocessor/filter.hpp" +#include "pointcloud_preprocessor/utility/geometry.hpp" + == == == == #include "autoware/pointcloud_preprocessor/filter.hpp" #include "autoware/pointcloud_preprocessor/utility/geometry.hpp" + >>>>>>>> original / + main : sensing / autoware_pointcloud_preprocessor / include / autoware / pointcloud_preprocessor / + polygon_remover / + polygon_remover.hpp #include #include @@ -27,39 +37,39 @@ #include -namespace autoware::pointcloud_preprocessor + namespace autoware::pointcloud_preprocessor { -class PolygonRemoverComponent : public autoware::pointcloud_preprocessor::Filter -{ -protected: - virtual void filter( - const PointCloud2ConstPtr & input, const IndicesPtr & indices, PointCloud2 & output); + class PolygonRemoverComponent : public autoware::pointcloud_preprocessor::Filter + { + protected: + virtual void filter( + const PointCloud2ConstPtr & input, const IndicesPtr & indices, PointCloud2 & output); - void publishRemovedPolygon(); + void publishRemovedPolygon(); - void update_polygon(const geometry_msgs::msg::Polygon::ConstSharedPtr & polygon_in); - static PolygonCgal polygon_geometry_to_cgal( - const geometry_msgs::msg::Polygon::ConstSharedPtr & polygon_in); - PointCloud2 remove_updated_polygon_from_cloud(const PointCloud2ConstPtr & cloud_in); - PointCloud2 remove_polygon_cgal_from_cloud( - const PointCloud2::ConstSharedPtr & cloud_in_ptr, const PolygonCgal & polyline_polygon); + void update_polygon(const geometry_msgs::msg::Polygon::ConstSharedPtr & polygon_in); + static PolygonCgal polygon_geometry_to_cgal( + const geometry_msgs::msg::Polygon::ConstSharedPtr & polygon_in); + PointCloud2 remove_updated_polygon_from_cloud(const PointCloud2ConstPtr & cloud_in); + PointCloud2 remove_polygon_cgal_from_cloud( + const PointCloud2::ConstSharedPtr & cloud_in_ptr, const PolygonCgal & polyline_polygon); -private: - rclcpp::Parameter param; - std::vector polygon_vertices_; - geometry_msgs::msg::Polygon::SharedPtr polygon_; + private: + rclcpp::Parameter param; + std::vector polygon_vertices_; + geometry_msgs::msg::Polygon::SharedPtr polygon_; - bool polygon_is_initialized_; - bool will_visualize_; - PolygonCgal polygon_cgal_; - visualization_msgs::msg::Marker marker_; + bool polygon_is_initialized_; + bool will_visualize_; + PolygonCgal polygon_cgal_; + visualization_msgs::msg::Marker marker_; - rclcpp::Publisher::SharedPtr pub_marker_ptr_; + rclcpp::Publisher::SharedPtr pub_marker_ptr_; -public: - PCL_MAKE_ALIGNED_OPERATOR_NEW - explicit PolygonRemoverComponent(const rclcpp::NodeOptions & options); -}; + public: + PCL_MAKE_ALIGNED_OPERATOR_NEW + explicit PolygonRemoverComponent(const rclcpp::NodeOptions & options); + }; } // namespace autoware::pointcloud_preprocessor #endif // AUTOWARE__POINTCLOUD_PREPROCESSOR__POLYGON_REMOVER__POLYGON_REMOVER_HPP_ diff --git a/sensing/autoware_pointcloud_preprocessor/include/autoware/pointcloud_preprocessor/utility/geometry.hpp b/sensing/autoware_pointcloud_preprocessor/include/autoware/pointcloud_preprocessor/utility/geometry.hpp index e4aacb1a80d6c..7f31d6ae9e019 100644 --- a/sensing/autoware_pointcloud_preprocessor/include/autoware/pointcloud_preprocessor/utility/geometry.hpp +++ b/sensing/autoware_pointcloud_preprocessor/include/autoware/pointcloud_preprocessor/utility/geometry.hpp @@ -12,8 +12,18 @@ // See the License for the specific language governing permissions and // limitations under the License. +<<<<<<<< + HEAD : sensing / autoware_pointcloud_preprocessor / include / pointcloud_preprocessor / utility / + geometry.hpp #ifndef AUTOWARE__POINTCLOUD_PREPROCESSOR__UTILITY__GEOMETRY_HPP_ #define AUTOWARE__POINTCLOUD_PREPROCESSOR__UTILITY__GEOMETRY_HPP_ + == == == == +#ifndef AUTOWARE__POINTCLOUD_PREPROCESSOR__UTILITY__GEOMETRY_HPP_ +#define AUTOWARE__POINTCLOUD_PREPROCESSOR__UTILITY__GEOMETRY_HPP_ + >>>>>>>> original / + main : sensing / autoware_pointcloud_preprocessor / include / autoware / pointcloud_preprocessor / + utility / + geometry.hpp #include #include @@ -29,7 +39,7 @@ #include #include -using K = CGAL::Exact_predicates_inexact_constructions_kernel; + using K = CGAL::Exact_predicates_inexact_constructions_kernel; using PointCgal = K::Point_2; using PolygonCgal = std::vector; @@ -81,6 +91,14 @@ void remove_polygon_cgal_from_cloud( bool point_within_cgal_polys( const pcl::PointXYZ & point, const std::vector & polyline_polygons); +< < < < < < < < HEAD : sensing / autoware_pointcloud_preprocessor / include / + pointcloud_preprocessor / utility / geometry.hpp +} // namespace pointcloud_preprocessor::utils + +#endif // POINTCLOUD_PREPROCESSOR__UTILITY__GEOMETRY_HPP_ +== == == == } // namespace autoware::pointcloud_preprocessor::utils #endif // AUTOWARE__POINTCLOUD_PREPROCESSOR__UTILITY__GEOMETRY_HPP_ +>>>>>>>> original / main : sensing / autoware_pointcloud_preprocessor / include / autoware / + pointcloud_preprocessor / utility / geometry.hpp diff --git a/sensing/autoware_pointcloud_preprocessor/include/autoware/pointcloud_preprocessor/vector_map_filter/vector_map_inside_area_filter.hpp b/sensing/autoware_pointcloud_preprocessor/include/autoware/pointcloud_preprocessor/vector_map_filter/vector_map_inside_area_filter.hpp index a6b497b40cd77..143185afe48a7 100644 --- a/sensing/autoware_pointcloud_preprocessor/include/autoware/pointcloud_preprocessor/vector_map_filter/vector_map_inside_area_filter.hpp +++ b/sensing/autoware_pointcloud_preprocessor/include/autoware/pointcloud_preprocessor/vector_map_filter/vector_map_inside_area_filter.hpp @@ -15,8 +15,18 @@ #ifndef AUTOWARE__POINTCLOUD_PREPROCESSOR__VECTOR_MAP_FILTER__VECTOR_MAP_INSIDE_AREA_FILTER_HPP_ #define AUTOWARE__POINTCLOUD_PREPROCESSOR__VECTOR_MAP_FILTER__VECTOR_MAP_INSIDE_AREA_FILTER_HPP_ +<<<<<<<< + HEAD : sensing / autoware_pointcloud_preprocessor / include / pointcloud_preprocessor / + vector_map_filter / vector_map_inside_area_filter.hpp +#include "pointcloud_preprocessor/filter.hpp" +#include "pointcloud_preprocessor/utility/geometry.hpp" + == == == == #include "autoware/pointcloud_preprocessor/filter.hpp" #include "autoware/pointcloud_preprocessor/utility/geometry.hpp" + >>>>>>>> original / + main : sensing / autoware_pointcloud_preprocessor / include / autoware / pointcloud_preprocessor / + vector_map_filter / + vector_map_inside_area_filter.hpp #include #include @@ -26,7 +36,7 @@ #include -using autoware::universe_utils::MultiPoint2d; + using autoware::universe_utils::MultiPoint2d; namespace autoware::pointcloud_preprocessor { diff --git a/sensing/autoware_pointcloud_preprocessor/launch/approximate_downsample_filter.launch.xml b/sensing/autoware_pointcloud_preprocessor/launch/approximate_downsample_filter.launch.xml new file mode 100644 index 0000000000000..23ae90281b306 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/launch/approximate_downsample_filter.launch.xml @@ -0,0 +1,16 @@ + + + + + + + + + + + + + + + + diff --git a/sensing/autoware_pointcloud_preprocessor/launch/crop_box_filter.launch.xml b/sensing/autoware_pointcloud_preprocessor/launch/crop_box_filter.launch.xml new file mode 100644 index 0000000000000..c699c92e22866 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/launch/crop_box_filter.launch.xml @@ -0,0 +1,16 @@ + + + + + + + + + + + + + + + + diff --git a/sensing/autoware_pointcloud_preprocessor/launch/distortion_corrector_node.launch.xml b/sensing/autoware_pointcloud_preprocessor/launch/distortion_corrector_node.launch.xml index bde9ca9e59ddd..be2a0e430f1a4 100644 --- a/sensing/autoware_pointcloud_preprocessor/launch/distortion_corrector_node.launch.xml +++ b/sensing/autoware_pointcloud_preprocessor/launch/distortion_corrector_node.launch.xml @@ -5,8 +5,13 @@ +<<<<<<< HEAD + + +======= +>>>>>>> original/main diff --git a/sensing/autoware_pointcloud_preprocessor/launch/dual_return_outlier_filter.launch.xml b/sensing/autoware_pointcloud_preprocessor/launch/dual_return_outlier_filter.launch.xml index c65996fbcdc65..4afc4906f0b3a 100644 --- a/sensing/autoware_pointcloud_preprocessor/launch/dual_return_outlier_filter.launch.xml +++ b/sensing/autoware_pointcloud_preprocessor/launch/dual_return_outlier_filter.launch.xml @@ -1,21 +1,24 @@ - +<<<<<<<< HEAD:sensing/autoware_pointcloud_preprocessor/launch/dual_return_outlier_filter_node.launch.xml + + + +======== +>>>>>>>> original/main:sensing/autoware_pointcloud_preprocessor/launch/dual_return_outlier_filter.launch.xml - - - - + + diff --git a/sensing/autoware_pointcloud_preprocessor/launch/lanelet2_map_filter.launch.xml b/sensing/autoware_pointcloud_preprocessor/launch/lanelet2_map_filter.launch.xml new file mode 100644 index 0000000000000..dafd5c283b072 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/launch/lanelet2_map_filter.launch.xml @@ -0,0 +1,6 @@ + + + + + + diff --git a/sensing/autoware_pointcloud_preprocessor/launch/pickup_based_voxel_grid_downsample_filter.launch.xml b/sensing/autoware_pointcloud_preprocessor/launch/pickup_based_voxel_grid_downsample_filter.launch.xml new file mode 100644 index 0000000000000..b606c1eb0d3fd --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/launch/pickup_based_voxel_grid_downsample_filter.launch.xml @@ -0,0 +1,16 @@ + + + + + + + + + + + + + + + + diff --git a/sensing/autoware_pointcloud_preprocessor/launch/pointcloud_accumulator.launch.xml b/sensing/autoware_pointcloud_preprocessor/launch/pointcloud_accumulator.launch.xml new file mode 100644 index 0000000000000..1269b1bdf4c99 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/launch/pointcloud_accumulator.launch.xml @@ -0,0 +1,16 @@ + + + + + + + + + + + + + + + + diff --git a/sensing/autoware_pointcloud_preprocessor/launch/polygon_remover.launch.py b/sensing/autoware_pointcloud_preprocessor/launch/polygon_remover.launch.py index 801642bac79ac..db58cac9d03c1 100644 --- a/sensing/autoware_pointcloud_preprocessor/launch/polygon_remover.launch.py +++ b/sensing/autoware_pointcloud_preprocessor/launch/polygon_remover.launch.py @@ -21,27 +21,32 @@ import yaml +def load_parameter_dic(package_share_directory, yaml_file_path): + param_file = os.path.join(get_package_share_directory(package_share_directory), yaml_file_path) + + with open(param_file, "r") as file: + param_dic = yaml.safe_load(file)["/**"]["ros__parameters"] + + return param_dic + + def generate_launch_description(): ns = "pointcloud_preprocessor" pkg = "autoware_pointcloud_preprocessor" - param_file = os.path.join( - get_package_share_directory("autoware_vehicle_info_utils"), "config/polygon_remover.yaml" + polygon_remover_param = load_parameter_dic( + "autoware_vehicle_info_utils", "config/polygon_remover.yaml" ) - with open(param_file, "r") as f: - polygon_remover_param = yaml.safe_load(f)["/**"]["ros__parameters"] + param = load_parameter_dic("pointcloud_preprocessor", "config/filter_param_file.yaml") + + param.update(polygon_remover_param) my_component = ComposableNode( package=pkg, plugin="autoware::pointcloud_preprocessor::PolygonRemoverComponent", name="polygon_remover", - parameters=[ - { - "polygon_vertices": polygon_remover_param["polygon_vertices"], - "will_visualize": polygon_remover_param["will_visualize"], - } - ], + parameters=[param], ) # set container to run all required components in the same process diff --git a/sensing/autoware_pointcloud_preprocessor/launch/preprocessor.launch.py b/sensing/autoware_pointcloud_preprocessor/launch/preprocessor.launch.py index 108cf6d4c4213..ffe8e829aa8c2 100644 --- a/sensing/autoware_pointcloud_preprocessor/launch/preprocessor.launch.py +++ b/sensing/autoware_pointcloud_preprocessor/launch/preprocessor.launch.py @@ -12,6 +12,9 @@ # See the License for the specific language governing permissions and # limitations under the License. +import os + +from ament_index_python.packages import get_package_share_directory import launch from launch.actions import DeclareLaunchArgument from launch.actions import LogInfo @@ -20,6 +23,16 @@ from launch.substitutions import PythonExpression from launch_ros.actions import ComposableNodeContainer from launch_ros.descriptions import ComposableNode +import yaml + + +def load_parameter_dic(package_share_directory, yaml_file_path): + param_file = os.path.join(get_package_share_directory(package_share_directory), yaml_file_path) + + with open(param_file, "r") as file: + param_dic = yaml.safe_load(file)["/**"]["ros__parameters"] + + return param_dic def launch_setup(context, *args, **kwargs): @@ -33,6 +46,61 @@ def launch_setup(context, *args, **kwargs): separate_concatenate_node_and_time_sync_node.lower() == "true" ) + ## ---------------------- Set up Example parameters --------------------------- + ## you could modify thie part and change the parameter directly in .yaml file. + filter_param = load_parameter_dic("pointcloud_preprocessor", "config/filter_param_file.yaml") + + sync_and_concat_param = load_parameter_dic( + "pointcloud_preprocessor", "config/concatenate_and_time_sync_param_file.yaml" + ) + + time_sync_param = load_parameter_dic( + "pointcloud_preprocessor", "config/time_synchronizer_param_file.yaml" + ) + + concat_param = load_parameter_dic( + "pointcloud_preprocessor", "config/concatenate_pointclouds_param_file.yaml" + ) + + crop_box_param = load_parameter_dic( + "pointcloud_preprocessor", "config/crop_box_filter_param_file.yaml" + ) + + sync_and_concat_updated_param = { + "input_topics": LaunchConfiguration("input_points_raw_list"), + "output_frame": LaunchConfiguration("tf_output_frame"), + "approximate_sync": True, + "publish_synchronized_pointcloud": False, + } + updated_param = { + "input_topics": LaunchConfiguration("input_points_raw_list"), + "output_frame": LaunchConfiguration("tf_output_frame"), + "approximate_sync": True, + } + crop_box_updated_param = { + "input_frame": LaunchConfiguration("tf_output_frame"), + "output_frame": LaunchConfiguration("tf_output_frame"), + "min_x": -200.0, + "max_x": 1000.0, + "min_y": -50.0, + "max_y": 50.0, + "min_z": -2.0, + "max_z": 3.0, + "negative": False, + } + + sync_and_concat_updated = {**filter_param, **sync_and_concat_param} + sync_and_concat_updated.update(sync_and_concat_updated_param) + + time_sync_param.update(updated_param) + + concat_param_updated = {**filter_param, **concat_param} + concat_param_updated.update(updated_param) + + crop_box_updated = {**filter_param, **crop_box_param} + crop_box_updated.update(crop_box_updated_param) + ## ---------------------- -------------------------- --------------------------- + if not is_separate_concatenate_node_and_time_sync_node: sync_and_concat_component = ComposableNode( package=pkg, @@ -42,15 +110,7 @@ def launch_setup(context, *args, **kwargs): ("~/input/twist", "/sensing/vehicle_velocity_converter/twist_with_covariance"), ("output", "points_raw/concatenated"), ], - parameters=[ - { - "input_topics": LaunchConfiguration("input_points_raw_list"), - "output_frame": LaunchConfiguration("tf_output_frame"), - "approximate_sync": True, - "publish_synchronized_pointcloud": False, - "input_twist_topic_type": "twist", - } - ], + parameters=[sync_and_concat_updated], ) concat_components = [sync_and_concat_component] else: @@ -62,13 +122,7 @@ def launch_setup(context, *args, **kwargs): ("~/input/twist", "/sensing/vehicle_velocity_converter/twist_with_covariance"), ("output", "points_raw/concatenated"), ], - parameters=[ - { - "input_topics": LaunchConfiguration("input_points_raw_list"), - "output_frame": LaunchConfiguration("tf_output_frame"), - "approximate_sync": True, - } - ], + parameters=[time_sync_param], ) concat_component = ComposableNode( @@ -76,13 +130,7 @@ def launch_setup(context, *args, **kwargs): plugin="autoware::pointcloud_preprocessor::PointCloudConcatenationComponent", name="concatenate_filter", remappings=[("output", "points_raw/concatenated")], - parameters=[ - { - "input_topics": LaunchConfiguration("input_points_raw_list"), - "output_frame": LaunchConfiguration("tf_output_frame"), - "approximate_sync": True, - } - ], + parameters=[concat_param_updated], ) concat_components = [time_sync_component, concat_component] @@ -106,19 +154,7 @@ def launch_setup(context, *args, **kwargs): ), ("output", LaunchConfiguration("output_points_raw")), ], - parameters=[ - { - "input_frame": LaunchConfiguration("tf_output_frame"), - "output_frame": LaunchConfiguration("tf_output_frame"), - "min_x": -200.0, - "max_x": 1000.0, - "min_y": -50.0, - "max_y": 50.0, - "min_z": -2.0, - "max_z": 3.0, - "negative": False, - } - ], + parameters=[crop_box_updated], ) # set container to run all required components in the same process diff --git a/sensing/autoware_pointcloud_preprocessor/launch/radius_search_2d_outlier_filter.launch.xml b/sensing/autoware_pointcloud_preprocessor/launch/radius_search_2d_outlier_filter.launch.xml new file mode 100644 index 0000000000000..38fa110716a15 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/launch/radius_search_2d_outlier_filter.launch.xml @@ -0,0 +1,16 @@ + + + + + + + + + + + + + + + + diff --git a/sensing/autoware_pointcloud_preprocessor/launch/random_downsample_filter.launch.xml b/sensing/autoware_pointcloud_preprocessor/launch/random_downsample_filter.launch.xml index c9d0bc3ce0608..aa7c96156ae4c 100644 --- a/sensing/autoware_pointcloud_preprocessor/launch/random_downsample_filter.launch.xml +++ b/sensing/autoware_pointcloud_preprocessor/launch/random_downsample_filter.launch.xml @@ -1,17 +1,21 @@ - - +<<<<<<<< HEAD:sensing/autoware_pointcloud_preprocessor/launch/random_downsample_filter_node.launch.xml + + + +======== +>>>>>>>> original/main:sensing/autoware_pointcloud_preprocessor/launch/random_downsample_filter.launch.xml - - + + diff --git a/sensing/autoware_pointcloud_preprocessor/launch/ring_outlier_filter.launch.xml b/sensing/autoware_pointcloud_preprocessor/launch/ring_outlier_filter.launch.xml new file mode 100644 index 0000000000000..545808e2787b4 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/launch/ring_outlier_filter.launch.xml @@ -0,0 +1,16 @@ + + + + + + + + + + + + + + + + diff --git a/sensing/autoware_pointcloud_preprocessor/launch/ring_passthrough_filter.launch.xml b/sensing/autoware_pointcloud_preprocessor/launch/ring_passthrough_filter.launch.xml index a9ed23e9faa3a..5bffc6f3e0463 100644 --- a/sensing/autoware_pointcloud_preprocessor/launch/ring_passthrough_filter.launch.xml +++ b/sensing/autoware_pointcloud_preprocessor/launch/ring_passthrough_filter.launch.xml @@ -1,24 +1,20 @@ - - - - - - +<<<<<<<< HEAD:sensing/autoware_pointcloud_preprocessor/launch/ring_passthrough_filter_node.launch.xml + + + + + +======== +>>>>>>>> original/main:sensing/autoware_pointcloud_preprocessor/launch/ring_passthrough_filter.launch.xml - - - - - - diff --git a/sensing/autoware_pointcloud_preprocessor/launch/vector_map_inside_area_filter.launch.xml b/sensing/autoware_pointcloud_preprocessor/launch/vector_map_inside_area_filter.launch.xml new file mode 100644 index 0000000000000..710b8175608bf --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/launch/vector_map_inside_area_filter.launch.xml @@ -0,0 +1,12 @@ + + + + + + + + + + + + diff --git a/sensing/autoware_pointcloud_preprocessor/launch/voxel_grid_downsample_filter.launch.xml b/sensing/autoware_pointcloud_preprocessor/launch/voxel_grid_downsample_filter.launch.xml new file mode 100644 index 0000000000000..c9faf2f855b32 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/launch/voxel_grid_downsample_filter.launch.xml @@ -0,0 +1,16 @@ + + + + + + + + + + + + + + + + diff --git a/sensing/autoware_pointcloud_preprocessor/launch/voxel_grid_outlier_filter.launch.xml b/sensing/autoware_pointcloud_preprocessor/launch/voxel_grid_outlier_filter.launch.xml new file mode 100644 index 0000000000000..a1afda22c9e3c --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/launch/voxel_grid_outlier_filter.launch.xml @@ -0,0 +1,16 @@ + + + + + + + + + + + + + + + + diff --git a/sensing/autoware_pointcloud_preprocessor/schema/approximate_downsample_filter.schema.json b/sensing/autoware_pointcloud_preprocessor/schema/approximate_downsample_filter.schema.json new file mode 100644 index 0000000000000..5590f3ab2eef3 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/schema/approximate_downsample_filter.schema.json @@ -0,0 +1,43 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Parameter for approximate downsample filter", + "type": "object", + "definitions": { + "approximate_downsample_filter": { + "type": "object", + "properties": { + "voxel_size_x": { + "type": "number", + "description": "the voxel size along x-axis [m]", + "default": "0.3" + }, + "voxel_size_y": { + "type": "number", + "description": "the voxel size along y-axis [m]", + "default": "0.3" + }, + "voxel_size_z": { + "type": "number", + "description": "the voxel size along z-axis [m]", + "default": "0.1" + } + }, + "required": ["voxel_size_x", "voxel_size_y", "voxel_size_z"], + "additionalProperties": false + } + }, + "properties": { + "/**": { + "type": "object", + "properties": { + "ros__parameters": { + "$ref": "#/definitions/approximate_downsample_filter" + } + }, + "required": ["ros__parameters"], + "additionalProperties": false + } + }, + "required": ["/**"], + "additionalProperties": false +} diff --git a/sensing/autoware_pointcloud_preprocessor/schema/blockage_diag.schema.json b/sensing/autoware_pointcloud_preprocessor/schema/blockage_diag.schema.json new file mode 100644 index 0000000000000..567b99ea5fad4 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/schema/blockage_diag.schema.json @@ -0,0 +1,143 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Parameter for blockage diag filter", + "type": "object", + "definitions": { + "blockage_diag": { + "type": "object", + "properties": { + "blockage_ratio_threshold": { + "type": "number", + "description": "The threshold of blockage area ratio.If the blockage value exceeds this threshold, the diagnostic state will be set to ERROR.", + "default": "0.1" + }, + "blockage_count_threshold": { + "type": "number", + "description": "The threshold of number continuous blockage frames", + "default": "50" + }, + "blockage_buffering_frames": { + "type": "integer", + "description": "The number of buffering about blockage detection [range:1-200]", + "default": "2" + }, + "blockage_buffering_interval": { + "type": "integer", + "description": "The interval of buffering about blockage detection", + "default": "1" + }, + "enable_dust_diag": { + "type": "boolean", + "description": "enable dust diag", + "default": "false" + }, + "publish_debug_image": { + "type": "boolean", + "description": "eanble debug", + "default": "false" + }, + "dust_ratio_threshold": { + "type": "number", + "description": "The threshold of dusty area ratio", + "default": "0.2" + }, + "dust_count_threshold": { + "type": "integer", + "description": "The threshold of number continuous frames include dusty area", + "default": "10" + }, + "dust_kernel_size": { + "type": "integer", + "description": "The kernel size of morphology processing in dusty area detection", + "default": "2" + }, + "dust_buffering_frames": { + "type": "integer", + "description": "The number of buffering about dusty area detection [range:1-200]", + "default": "10" + }, + "dust_buffering_interval": { + "type": "integer", + "description": "The interval of buffering about dusty area detection", + "default": "1" + }, + "max_distance_range": { + "type": "number", + "description": "Maximum view range for the LiDAR", + "default": "200.0" + }, + "horizontal_resolution": { + "type": "number", + "description": "The horizontal resolution of depth map image [deg/pixel]", + "default": "0.4" + }, + "blockage_kernel": { + "type": "integer", + "description": "The kernel size of morphology processing the detected blockage area", + "default": "10" + }, + "angle_range": { + "type": "array", + "description": "The effective range of LiDAR", + "minItems": 2, + "maxItems": 2, + "items": { + "type": "number", + "minimum": 0.0, + "maximum": 360.0 + } + }, + "vertical_bins": { + "type": "integer", + "description": "The LiDAR channel", + "default": "40" + }, + "is_channel_order_top2down": { + "type": "boolean", + "description": "If the lidar channels are indexed from top to down", + "default": "true" + }, + "horizontal_ring_id": { + "type": "integer", + "description": "The id of horizontal ring of the LiDAR", + "default": "18" + } + }, + "required": [ + "blockage_ratio_threshold", + "blockage_count_threshold", + "blockage_buffering_frames", + "blockage_buffering_interval", + "enable_dust_diag", + "publish_debug_image", + "dust_ratio_threshold", + "dust_count_threshold", + "dust_kernel_size", + "dust_buffering_frames", + "dust_buffering_interval", + "max_distance_range", + "horizontal_resolution", + "blockage_kernel", + "angle_range", + "vertical_bins", + "is_channel_order_top2down", + "horizontal_ring_id" + ], + "additionalProperties": false + } + }, + "properties": { + "/**": { + "type": "object", + "properties": { + "ros__parameters": { + "$ref": "#/definitions/blockage_diag" + } + }, + "required": ["ros__parameters"], + "additionalProperties": false + } + }, + "required": ["/**"], + "additionalProperties": false +} diff --git a/sensing/autoware_pointcloud_preprocessor/schema/crop_box_filter.schema.json b/sensing/autoware_pointcloud_preprocessor/schema/crop_box_filter.schema.json new file mode 100644 index 0000000000000..f8d820d86ae37 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/schema/crop_box_filter.schema.json @@ -0,0 +1,58 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Parameter for crop box filter", + "type": "object", + "definitions": { + "crop_box_filter": { + "type": "object", + "properties": { + "min_x": { + "type": "number", + "description": "x-coordinate minimum value for crop range", + "default": "-1.0" + }, + "min_y": { + "type": "number", + "description": "y-coordinate minimum value for crop range", + "default": "-1.0" + }, + "min_z": { + "type": "number", + "description": "z-coordinate minimum value for crop range", + "default": "-1.0" + }, + "max_x": { + "type": "number", + "description": "x-coordinate maximum value for crop range", + "default": "1.0" + }, + "max_y": { + "type": "number", + "description": "y-coordinate maximum value for crop range", + "default": "1.0" + }, + "max_z": { + "type": "number", + "description": "z-coordinate maximum value for crop range", + "default": "1.0" + } + }, + "required": ["min_x", "min_y", "min_z", "max_x", "max_y", "max_z"], + "additionalProperties": false + } + }, + "properties": { + "/**": { + "type": "object", + "properties": { + "ros__parameters": { + "$ref": "#/definitions/crop_box_filter" + } + }, + "required": ["ros__parameters"], + "additionalProperties": false + } + }, + "required": ["/**"], + "additionalProperties": false +} diff --git a/sensing/autoware_pointcloud_preprocessor/schema/dual_return_outlier_filter.schema.json b/sensing/autoware_pointcloud_preprocessor/schema/dual_return_outlier_filter.schema.json new file mode 100644 index 0000000000000..726b0eac07624 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/schema/dual_return_outlier_filter.schema.json @@ -0,0 +1,131 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Parameter for dual return outlier filter", + "type": "object", + "definitions": { + "dual_return_outlier_filter": { + "type": "object", + "properties": { + "x_max": { + "type": "number", + "description": "Maximum of x for `Fixed_xyz_ROI` mode", + "default": "18.0" + }, + "x_min": { + "type": "number", + "description": "Minimum of x for `Fixed_xyz_ROI` mode", + "default": "-12.0" + }, + "y_max": { + "type": "number", + "description": "Maximum of y for `Fixed_xyz_ROI` mode", + "default": "2.0" + }, + "y_min": { + "type": "number", + "description": "Minimum of y for `Fixed_xyz_ROI` mode", + "default": "-2.0" + }, + "z_max": { + "type": "number", + "description": "Maximum of z for `Fixed_xyz_ROI` mode", + "default": "10.0" + }, + "z_min": { + "type": "number", + "description": "Minimum of z for `Fixed_xyz_ROI` mode", + "default": "0.0" + }, + "min_azimuth_deg": { + "type": "number", + "description": "The left limit of azimuth for `Fixed_azimuth_ROI` mode", + "default": "135.0" + }, + "max_azimuth_deg": { + "type": "number", + "description": "The right limit of azimuth for `Fixed_azimuth_ROI` mode", + "default": "225.0" + }, + "max_distance": { + "type": "number", + "description": "The limit distance for for `Fixed_azimuth_ROI` mode", + "default": "12.0" + }, + "vertical_bins": { + "type": "integer", + "description": "The number of vertical bin for visibility histogram", + "default": "128" + }, + "max_azimuth_diff": { + "type": "number", + "description": "Threshold for ring_outlier_filter", + "default": "50" + }, + "weak_first_distance_ratio": { + "type": "number", + "description": "Threshold for ring_outlier_filter", + "default": "1.004" + }, + "general_distance_ratio": { + "type": "number", + "description": "Threshold for ring_outlier_filter", + "default": "1.03" + }, + "weak_first_local_noise_threshold": { + "type": "integer", + "description": "The parameter for determining whether it is noise", + "default": "2" + }, + "roi_mode": { + "type": "string", + "description": "roi mode", + "default": "Fixed_xyz_ROI" + }, + "visibility_error_threshold": { + "type": "number", + "description": "When the percentage of white pixels in the binary histogram falls below this parameter the diagnostic status becomes ERR", + "default": "0.5" + }, + "visibility_warn_threshold": { + "type": "number", + "description": "When the percentage of white pixels in the binary histogram falls below this parameter the diagnostic status becomes WARN", + "default": "0.7" + } + }, + "required": [ + "x_max", + "x_min", + "y_max", + "y_min", + "z_max", + "z_min", + "min_azimuth_deg", + "max_azimuth_deg", + "max_distance", + "vertical_bins", + "max_azimuth_diff", + "weak_first_distance_ratio", + "general_distance_ratio", + "weak_first_local_noise_threshold", + "roi_mode", + "visibility_error_threshold", + "visibility_warn_threshold" + ], + "additionalProperties": false + } + }, + "properties": { + "/**": { + "type": "object", + "properties": { + "ros__parameters": { + "$ref": "#/definitions/dual_return_outlier_filter" + } + }, + "required": ["ros__parameters"], + "additionalProperties": false + } + }, + "required": ["/**"], + "additionalProperties": false +} diff --git a/sensing/autoware_pointcloud_preprocessor/schema/filter.schema.json b/sensing/autoware_pointcloud_preprocessor/schema/filter.schema.json new file mode 100644 index 0000000000000..8e0a6605933b8 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/schema/filter.schema.json @@ -0,0 +1,66 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Parameter for filter", + "type": "object", + "definitions": { + "filter": { + "type": "object", + "properties": { + "input_frame": { + "type": "string", + "description": "input frame id", + "default": "" + }, + "output_frame": { + "type": "string", + "description": "output frame id", + "default": "" + }, + "max_queue_size": { + "type": "integer", + "minimum": 0, + "description": "max queue size of input/output topics", + "default": 5 + }, + "use_indices": { + "type": "boolean", + "description": "flag to use pointcloud indices", + "default": false + }, + "latched_indices": { + "type": "boolean", + "description": "flag to latch pointcloud indices", + "default": false + }, + "approximate_sync": { + "type": "boolean", + "description": "flag to use approximate sync option", + "default": false + } + }, + "required": [ + "input_frame", + "output_frame", + "max_queue_size", + "use_indices", + "latched_indices", + "approximate_sync" + ], + "additionalProperties": false + } + }, + "properties": { + "/**": { + "type": "object", + "properties": { + "ros__parameters": { + "$ref": "#/definitions/filter" + } + }, + "required": ["ros__parameters"], + "additionalProperties": false + } + }, + "required": ["/**"], + "additionalProperties": false +} diff --git a/sensing/autoware_pointcloud_preprocessor/schema/lanelet2_map_filter.schema.json b/sensing/autoware_pointcloud_preprocessor/schema/lanelet2_map_filter.schema.json new file mode 100644 index 0000000000000..af23858ddbb1b --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/schema/lanelet2_map_filter.schema.json @@ -0,0 +1,38 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Parameter for lanelet2 map filter", + "type": "object", + "definitions": { + "lanelet2_map_filter": { + "type": "object", + "properties": { + "voxel_size_x": { + "type": "number", + "description": "voxel size along x-axis [m]", + "default": "0.04" + }, + "voxel_size_y": { + "type": "number", + "description": "voxel size along y-axis [m]", + "default": "0.04" + } + }, + "required": ["voxel_size_x", "voxel_size_y"], + "additionalProperties": false + } + }, + "properties": { + "/**": { + "type": "object", + "properties": { + "ros__parameters": { + "$ref": "#/definitions/lanelet2_map_filter" + } + }, + "required": ["ros__parameters"], + "additionalProperties": false + } + }, + "required": ["/**"], + "additionalProperties": false +} diff --git a/sensing/autoware_pointcloud_preprocessor/schema/passthrough_filter.schema.json b/sensing/autoware_pointcloud_preprocessor/schema/passthrough_filter.schema.json new file mode 100644 index 0000000000000..d205509c2541f --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/schema/passthrough_filter.schema.json @@ -0,0 +1,59 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Parameter for passthrough filter", + "type": "object", + "definitions": { + "passthrough_filter": { + "type": "object", + "properties": { + "filter_limit_min": { + "type": "integer", + "description": "minimum allowed field value", + "default": "0" + }, + "filter_limit_max": { + "type": "integer", + "description": "maximum allowed field value", + "default": "127" + }, + "filter_field_name": { + "type": "string", + "description": "filtering field name", + "default": "ring" + }, + "keep_organized": { + "type": "boolean", + "description": "flag to keep indices structure", + "default": "false" + }, + "filter_limit_negative": { + "type": "boolean", + "description": "flag to return whether the data is inside limit or not", + "default": "false" + } + }, + "required": [ + "filter_limit_min", + "filter_limit_max", + "filter_field_name", + "keep_organized", + "filter_limit_negative" + ], + "additionalProperties": false + } + }, + "properties": { + "/**": { + "type": "object", + "properties": { + "ros__parameters": { + "$ref": "#/definitions/passthrough_filter" + } + }, + "required": ["ros__parameters"], + "additionalProperties": false + } + }, + "required": ["/**"], + "additionalProperties": false +} diff --git a/sensing/autoware_pointcloud_preprocessor/schema/pickup_based_voxel_grid_downsample_filter.schema.json b/sensing/autoware_pointcloud_preprocessor/schema/pickup_based_voxel_grid_downsample_filter.schema.json new file mode 100644 index 0000000000000..eec3617c59086 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/schema/pickup_based_voxel_grid_downsample_filter.schema.json @@ -0,0 +1,43 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Parameter for pickup based voxel grid downsample filter", + "type": "object", + "definitions": { + "pickup_based_voxel_grid_downsample_fitler": { + "type": "object", + "properties": { + "voxel_size_x": { + "type": "number", + "description": "voxel size along x-axis [m]", + "default": "1.0" + }, + "voxel_size_y": { + "type": "number", + "description": "voxel size along y-axis [m]", + "default": "1.0" + }, + "voxel_size_z": { + "type": "number", + "description": "voxel size along z-axis [m]", + "default": "1.0" + } + }, + "required": ["voxel_size_x", "voxel_size_y", "voxel_size_z"], + "additionalProperties": false + } + }, + "properties": { + "/**": { + "type": "object", + "properties": { + "ros__parameters": { + "$ref": "#/definitions/pickup_based_voxel_grid_downsample_fitler" + } + }, + "required": ["ros__parameters"], + "additionalProperties": false + } + }, + "required": ["/**"], + "additionalProperties": false +} diff --git a/sensing/autoware_pointcloud_preprocessor/schema/pointcloud_accumulator.schema.json b/sensing/autoware_pointcloud_preprocessor/schema/pointcloud_accumulator.schema.json new file mode 100644 index 0000000000000..79d21c5a15458 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/schema/pointcloud_accumulator.schema.json @@ -0,0 +1,38 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Parameter for pointcloud accumulator", + "type": "object", + "definitions": { + "pointcloud_accumulator": { + "type": "object", + "properties": { + "accumulation_time_sec": { + "type": "number", + "description": "accumulation period [s]", + "default": "2.0" + }, + "pointcloud_buffer_size": { + "type": "integer", + "description": "buffer size", + "default": "50" + } + }, + "required": ["accumulation_time_sec", "pointcloud_buffer_size"], + "additionalProperties": false + } + }, + "properties": { + "/**": { + "type": "object", + "properties": { + "ros__parameters": { + "$ref": "#/definitions/approximate_downsample_filter" + } + }, + "required": ["ros__parameters"], + "additionalProperties": false + } + }, + "required": ["/**"], + "additionalProperties": false +} diff --git a/sensing/autoware_pointcloud_preprocessor/schema/random_downsample_filter.schema.json b/sensing/autoware_pointcloud_preprocessor/schema/random_downsample_filter.schema.json new file mode 100644 index 0000000000000..f243c0eeb2aa5 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/schema/random_downsample_filter.schema.json @@ -0,0 +1,34 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Paramter for random downsample filter", + "type": "object", + "definitions": { + "random_downsample_filter": { + "type": "object", + "properties": { + "sample_num": { + "type": "integer", + "minimum": 0, + "description": "number of indices to be sampled", + "default": "1500" + } + }, + "required": ["sample_num"], + "additionalProperties": false + } + }, + "properties": { + "/**": { + "type": "object", + "properties": { + "ros__parameters": { + "$ref": "#/definitions/random_downsample_filter" + } + }, + "required": ["ros__parameters"], + "additionalProperties": false + } + }, + "required": ["/**"], + "additionalProperties": false +} diff --git a/sensing/autoware_pointcloud_preprocessor/schema/ring_outlier_filter.schema.json b/sensing/autoware_pointcloud_preprocessor/schema/ring_outlier_filter.schema.json new file mode 100644 index 0000000000000..06386f2fda251 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/schema/ring_outlier_filter.schema.json @@ -0,0 +1,103 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Parameter for ring outlier filter", + "type": "object", + "definitions": { + "ring_outlier_filter": { + "type": "object", + "properties": { + "distance_ratio": { + "type": "number", + "description": "distance_ratio", + "default": "1.03" + }, + "object_length_threshold": { + "type": "number", + "description": "object_length_threshold", + "default": "0.1" + }, + "num_points_threshold": { + "type": "integer", + "description": "num_points_threshold", + "default": "4" + }, + "max_rings_num": { + "type": "integer", + "minimum": 1, + "description": "max_rings_num", + "default": "128" + }, + "max_points_num_per_ring": { + "type": "integer", + "minimum": 0, + "description": "Set this value large enough such that HFoV / resolution < max_points_num_per_ring", + "default": "4000" + }, + "publish_outlier_pointcloud": { + "type": "boolean", + "description": "Flag to publish outlier pointcloud and visibility score. Due to performance concerns, please set to false during experiments.", + "default": "false" + }, + "min_azimuth_deg": { + "type": "number", + "description": "The left limit of azimuth for visibility score calculation", + "default": "0.0" + }, + "max_azimuth_deg": { + "type": "number", + "description": "The right limit of azimuth for visibility score calculation", + "default": "360.0" + }, + "max_distance": { + "type": "number", + "description": "The limit distance for visibility score calculation", + "default": "12.0" + }, + "vertical_bins": { + "type": "integer", + "description": "The number of vertical bin for visibility histogram", + "default": "128" + }, + "horizontal_bins": { + "type": "integer", + "description": "The number of horizontal bin for visibility histogram", + "default": "36" + }, + "noise_threshold": { + "type": "integer", + "description": "The threshold value for distinguishing noise from valid points in the frequency image", + "default": "2" + } + }, + "required": [ + "distance_ratio", + "object_length_threshold", + "num_points_threshold", + "max_rings_num", + "max_points_num_per_ring", + "publish_outlier_pointcloud", + "min_azimuth_deg", + "max_azimuth_deg", + "max_distance", + "vertical_bins", + "horizontal_bins", + "noise_threshold" + ], + "additionalProperties": false + } + }, + "properties": { + "/**": { + "type": "object", + "properties": { + "ros__parameters": { + "$ref": "#/definitions/ring_outlier_filter" + } + }, + "required": ["ros__parameters"], + "additionalProperties": false + } + }, + "required": ["/**"], + "additionalProperties": false +} diff --git a/sensing/autoware_pointcloud_preprocessor/schema/vector_map_inside_area_filter.schema.json b/sensing/autoware_pointcloud_preprocessor/schema/vector_map_inside_area_filter.schema.json new file mode 100644 index 0000000000000..e50549faf7331 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/schema/vector_map_inside_area_filter.schema.json @@ -0,0 +1,43 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Parameter for vector map inside area filter", + "type": "object", + "definitions": { + "vector_map_inside_area_filter": { + "type": "object", + "properties": { + "polygon_type": { + "type": "string", + "description": "polygon type to be filtered", + "default": "no_obstacle_segmentation_area" + }, + "use_z_filter": { + "type": "boolean", + "description": "use z value for filtering", + "default": "false" + }, + "z_threshold": { + "type": "number", + "description": "z threshold for filtering", + "default": "0.0" + } + }, + "required": ["polygon_type", "use_z_filter", "z_threshold"], + "additionalProperties": false + } + }, + "properties": { + "/**": { + "type": "object", + "properties": { + "ros__parameters": { + "$ref": "#/definitions/vector_map_inside_area_filter" + } + }, + "required": ["ros__parameters"], + "additionalProperties": false + } + }, + "required": ["/**"], + "additionalProperties": false +} diff --git a/sensing/autoware_pointcloud_preprocessor/schema/voxel_grid_downsample_filter.schema.json b/sensing/autoware_pointcloud_preprocessor/schema/voxel_grid_downsample_filter.schema.json new file mode 100644 index 0000000000000..6cb35812e5372 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/schema/voxel_grid_downsample_filter.schema.json @@ -0,0 +1,43 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Parameter for voxel grid downsample filter", + "type": "object", + "definitions": { + "voxel_grid_downsample_filter": { + "type": "object", + "properties": { + "voxel_size_x": { + "type": "number", + "description": "the voxel size along x-axis [m]", + "default": "0.3" + }, + "voxel_size_y": { + "type": "number", + "description": "the voxel size along y-axis [m]", + "default": "0.3" + }, + "voxel_size_z": { + "type": "number", + "description": "the voxel size along z-axis [m]", + "default": "0.1" + } + }, + "required": ["voxel_size_x", "voxel_size_y", "voxel_size_z"], + "additionalProperties": false + } + }, + "properties": { + "/**": { + "type": "object", + "properties": { + "ros__parameters": { + "$ref": "#/definitions/voxel_grid_downsample_filter" + } + }, + "required": ["ros__parameters"], + "additionalProperties": false + } + }, + "required": ["/**"], + "additionalProperties": false +} diff --git a/sensing/autoware_pointcloud_preprocessor/schema/voxel_grid_outlier_filter.schema.json b/sensing/autoware_pointcloud_preprocessor/schema/voxel_grid_outlier_filter.schema.json new file mode 100644 index 0000000000000..b108e550d9532 --- /dev/null +++ b/sensing/autoware_pointcloud_preprocessor/schema/voxel_grid_outlier_filter.schema.json @@ -0,0 +1,48 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Parameter for voxel grid outlier filter", + "type": "object", + "definitions": { + "voxel_grid_outlier_filter": { + "type": "object", + "properties": { + "voxel_size_x": { + "type": "number", + "description": "the voxel size along x-axis [m]", + "default": "0.3" + }, + "voxel_size_y": { + "type": "number", + "description": "the voxel size along y-axis [m]", + "default": "0.3" + }, + "voxel_size_z": { + "type": "number", + "description": "the voxel size along z-axis [m]", + "default": "0.1" + }, + "voxel_points_threshold": { + "type": "integer", + "description": "the minimum number of points in each voxel", + "default": "2" + } + }, + "required": ["voxel_size_x", "voxel_size_y", "voxel_size_z", "voxel_points_threshold"], + "additionalProperties": false + } + }, + "properties": { + "/**": { + "type": "object", + "properties": { + "ros__parameters": { + "$ref": "#/definitions/voxel_grid_outlier_filter" + } + }, + "required": ["ros__parameters"], + "additionalProperties": false + } + }, + "required": ["/**"], + "additionalProperties": false +} diff --git a/sensing/autoware_pointcloud_preprocessor/src/concatenate_data/concatenate_and_time_sync_nodelet.cpp b/sensing/autoware_pointcloud_preprocessor/src/concatenate_data/concatenate_and_time_sync_nodelet.cpp index 95c353212484c..8cce471cecf32 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/concatenate_data/concatenate_and_time_sync_nodelet.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/concatenate_data/concatenate_and_time_sync_nodelet.cpp @@ -52,6 +52,7 @@ #include "autoware/pointcloud_preprocessor/concatenate_data/concatenate_and_time_sync_nodelet.hpp" #include "autoware/pointcloud_preprocessor/utility/memory.hpp" +#include "pointcloud_preprocessor/utility/memory.hpp" #include @@ -73,7 +74,7 @@ namespace autoware::pointcloud_preprocessor PointCloudConcatenateDataSynchronizerComponent::PointCloudConcatenateDataSynchronizerComponent( const rclcpp::NodeOptions & node_options) : Node("point_cloud_concatenator_component", node_options), - input_twist_topic_type_(declare_parameter("input_twist_topic_type", "twist")) + input_twist_topic_type_(declare_parameter("input_twist_topic_type")) { // initialize debug tool { @@ -87,12 +88,12 @@ PointCloudConcatenateDataSynchronizerComponent::PointCloudConcatenateDataSynchro // Set parameters { - output_frame_ = static_cast(declare_parameter("output_frame", "")); + output_frame_ = declare_parameter("output_frame"); if (output_frame_.empty()) { RCLCPP_ERROR(get_logger(), "Need an 'output_frame' parameter to be set before continuing!"); return; } - declare_parameter("input_topics", std::vector()); + declare_parameter>("input_topics"); input_topics_ = get_parameter("input_topics").as_string_array(); if (input_topics_.empty()) { RCLCPP_ERROR(get_logger(), "Need a 'input_topics' parameter to be set before continuing!"); @@ -104,21 +105,21 @@ PointCloudConcatenateDataSynchronizerComponent::PointCloudConcatenateDataSynchro } // Optional parameters - maximum_queue_size_ = static_cast(declare_parameter("max_queue_size", 5)); - timeout_sec_ = static_cast(declare_parameter("timeout_sec", 0.1)); + maximum_queue_size_ = declare_parameter("max_queue_size"); + timeout_sec_ = declare_parameter("timeout_sec"); - input_offset_ = declare_parameter("input_offset", std::vector{}); + input_offset_ = declare_parameter>("input_offset"); if (!input_offset_.empty() && input_topics_.size() != input_offset_.size()) { RCLCPP_ERROR(get_logger(), "The number of topics does not match the number of offsets."); return; } // Check if publish synchronized pointcloud - publish_synchronized_pointcloud_ = declare_parameter("publish_synchronized_pointcloud", true); + publish_synchronized_pointcloud_ = declare_parameter("publish_synchronized_pointcloud"); keep_input_frame_in_synchronized_pointcloud_ = - declare_parameter("keep_input_frame_in_synchronized_pointcloud", true); + declare_parameter("keep_input_frame_in_synchronized_pointcloud"); synchronized_pointcloud_postfix_ = - declare_parameter("synchronized_pointcloud_postfix", "pointcloud"); + declare_parameter("synchronized_pointcloud_postfix"); } // Initialize not_subscribed_topic_names_ diff --git a/sensing/autoware_pointcloud_preprocessor/src/concatenate_data/concatenate_pointclouds.cpp b/sensing/autoware_pointcloud_preprocessor/src/concatenate_data/concatenate_pointclouds.cpp index 24c4b572181fe..41f9131402685 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/concatenate_data/concatenate_pointclouds.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/concatenate_data/concatenate_pointclouds.cpp @@ -15,6 +15,7 @@ #include "autoware/pointcloud_preprocessor/concatenate_data/concatenate_pointclouds.hpp" #include "autoware/pointcloud_preprocessor/utility/memory.hpp" +#include "pointcloud_preprocessor/utility/memory.hpp" #include @@ -49,12 +50,12 @@ PointCloudConcatenationComponent::PointCloudConcatenationComponent( // Set parameters { - output_frame_ = static_cast(declare_parameter("output_frame", "")); + output_frame_ = declare_parameter("output_frame"); if (output_frame_.empty()) { RCLCPP_ERROR(get_logger(), "Need an 'output_frame' parameter to be set before continuing!"); return; } - declare_parameter("input_topics", std::vector()); + declare_parameter>("input_topics"); input_topics_ = get_parameter("input_topics").as_string_array(); if (input_topics_.empty()) { RCLCPP_ERROR(get_logger(), "Need a 'input_topics' parameter to be set before continuing!"); @@ -66,11 +67,11 @@ PointCloudConcatenationComponent::PointCloudConcatenationComponent( } // Optional parameters - maximum_queue_size_ = static_cast(declare_parameter("max_queue_size", 5)); + maximum_queue_size_ = declare_parameter("max_queue_size"); /** input pointclouds should be */ - timeout_sec_ = static_cast(declare_parameter("timeout_sec", 0.033)); + timeout_sec_ = declare_parameter("timeout_sec"); - input_offset_ = declare_parameter("input_offset", std::vector{}); + input_offset_ = declare_parameter>("input_offset"); if (!input_offset_.empty() && input_topics_.size() != input_offset_.size()) { RCLCPP_ERROR(get_logger(), "The number of topics does not match the number of offsets."); return; diff --git a/sensing/autoware_pointcloud_preprocessor/src/crop_box_filter/crop_box_filter_nodelet.cpp b/sensing/autoware_pointcloud_preprocessor/src/crop_box_filter/crop_box_filter_nodelet.cpp index a09b6bfb6a1ce..349ed0946f499 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/crop_box_filter/crop_box_filter_nodelet.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/crop_box_filter/crop_box_filter_nodelet.cpp @@ -73,13 +73,13 @@ CropBoxFilterComponent::CropBoxFilterComponent(const rclcpp::NodeOptions & optio // set initial parameters { auto & p = param_; - p.min_x = static_cast(declare_parameter("min_x", -1.0)); - p.min_y = static_cast(declare_parameter("min_y", -1.0)); - p.min_z = static_cast(declare_parameter("min_z", -1.0)); - p.max_x = static_cast(declare_parameter("max_x", 1.0)); - p.max_y = static_cast(declare_parameter("max_y", 1.0)); - p.max_z = static_cast(declare_parameter("max_z", 1.0)); - p.negative = static_cast(declare_parameter("negative", false)); + p.min_x = declare_parameter("min_x"); + p.min_y = declare_parameter("min_y"); + p.min_z = declare_parameter("min_z"); + p.max_x = declare_parameter("max_x"); + p.max_y = declare_parameter("max_y"); + p.max_z = declare_parameter("max_z"); + p.negative = declare_parameter("negative"); if (tf_input_frame_.empty()) { throw std::invalid_argument("Crop box requires non-empty input_frame"); } diff --git a/sensing/autoware_pointcloud_preprocessor/src/distortion_corrector/distortion_corrector.cpp b/sensing/autoware_pointcloud_preprocessor/src/distortion_corrector/distortion_corrector.cpp index 652edd9126358..d742e49b11d55 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/distortion_corrector/distortion_corrector.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/distortion_corrector/distortion_corrector.cpp @@ -12,455 +12,470 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "autoware/pointcloud_preprocessor/distortion_corrector/distortion_corrector.hpp" +<<<<<<< HEAD +#include "pointcloud_preprocessor/distortion_corrector/distortion_corrector.hpp" +#include "pointcloud_preprocessor/utility/memory.hpp" +======= +#include "autoware/pointcloud_preprocessor/distortion_corrector/distortion_corrector.hpp" #include "autoware/pointcloud_preprocessor/utility/memory.hpp" + >>>>>>> original/main #include #include + <<<<<<< HEAD namespace pointcloud_preprocessor +======= namespace autoware::pointcloud_preprocessor +>>>>>>> original/main { + template + bool DistortionCorrector::pointcloud_transform_exists() + { + return pointcloud_transform_exists_; + } -template -bool DistortionCorrector::pointcloud_transform_exists() -{ - return pointcloud_transform_exists_; -} - -template -bool DistortionCorrector::pointcloud_transform_needed() -{ - return pointcloud_transform_needed_; -} + template + bool DistortionCorrector::pointcloud_transform_needed() + { + return pointcloud_transform_needed_; + } -template -std::deque DistortionCorrector::get_twist_queue() -{ - return twist_queue_; -} + template + std::deque DistortionCorrector::get_twist_queue() + { + return twist_queue_; + } -template -std::deque DistortionCorrector::get_angular_velocity_queue() -{ - return angular_velocity_queue_; -} + template + std::deque + DistortionCorrector::get_angular_velocity_queue() + { + return angular_velocity_queue_; + } -template -void DistortionCorrector::processTwistMessage( - const geometry_msgs::msg::TwistWithCovarianceStamped::ConstSharedPtr twist_msg) -{ - geometry_msgs::msg::TwistStamped msg; - msg.header = twist_msg->header; - msg.twist = twist_msg->twist.twist; - twist_queue_.push_back(msg); - - while (!twist_queue_.empty()) { - // for replay rosbag - if (rclcpp::Time(twist_queue_.front().header.stamp) > rclcpp::Time(twist_msg->header.stamp)) { - twist_queue_.pop_front(); - } else if ( // NOLINT - rclcpp::Time(twist_queue_.front().header.stamp) < - rclcpp::Time(twist_msg->header.stamp) - rclcpp::Duration::from_seconds(1.0)) { - twist_queue_.pop_front(); - } else { - break; + template + void DistortionCorrector::processTwistMessage( + const geometry_msgs::msg::TwistWithCovarianceStamped::ConstSharedPtr twist_msg) + { + geometry_msgs::msg::TwistStamped msg; + msg.header = twist_msg->header; + msg.twist = twist_msg->twist.twist; + twist_queue_.push_back(msg); + + while (!twist_queue_.empty()) { + // for replay rosbag + if (rclcpp::Time(twist_queue_.front().header.stamp) > rclcpp::Time(twist_msg->header.stamp)) { + twist_queue_.pop_front(); + } else if ( // NOLINT + rclcpp::Time(twist_queue_.front().header.stamp) < + rclcpp::Time(twist_msg->header.stamp) - rclcpp::Duration::from_seconds(1.0)) { + twist_queue_.pop_front(); + } else { + break; + } } } -} - -template -void DistortionCorrector::processIMUMessage( - const std::string & base_frame, const sensor_msgs::msg::Imu::ConstSharedPtr imu_msg) -{ - getIMUTransformation(base_frame, imu_msg->header.frame_id); - enqueueIMU(imu_msg); -} -template -void DistortionCorrector::getIMUTransformation( - const std::string & base_frame, const std::string & imu_frame) -{ - if (imu_transform_exists_) { - return; + template + void DistortionCorrector::processIMUMessage( + const std::string & base_frame, const sensor_msgs::msg::Imu::ConstSharedPtr imu_msg) + { + getIMUTransformation(base_frame, imu_msg->header.frame_id); + enqueueIMU(imu_msg); } - geometry_imu_to_base_link_ptr_ = std::make_shared(); + template + void DistortionCorrector::getIMUTransformation( + const std::string & base_frame, const std::string & imu_frame) + { + if (imu_transform_exists_) { + return; + } - tf2::Transform tf2_imu_to_base_link; - if (base_frame == imu_frame) { - tf2_imu_to_base_link.setOrigin(tf2::Vector3(0.0, 0.0, 0.0)); - tf2_imu_to_base_link.setRotation(tf2::Quaternion(0.0, 0.0, 0.0, 1.0)); - imu_transform_exists_ = true; - } else { - try { - const auto transform_msg = - tf_buffer_.lookupTransform(base_frame, imu_frame, tf2::TimePointZero); - tf2::convert(transform_msg.transform, tf2_imu_to_base_link); - imu_transform_exists_ = true; - } catch (const tf2::TransformException & ex) { - RCLCPP_WARN(node_->get_logger(), "%s", ex.what()); - RCLCPP_ERROR( - node_->get_logger(), "Please publish TF %s to %s", base_frame.c_str(), imu_frame.c_str()); + geometry_imu_to_base_link_ptr_ = std::make_shared(); + tf2::Transform tf2_imu_to_base_link; + if (base_frame == imu_frame) { tf2_imu_to_base_link.setOrigin(tf2::Vector3(0.0, 0.0, 0.0)); tf2_imu_to_base_link.setRotation(tf2::Quaternion(0.0, 0.0, 0.0, 1.0)); + imu_transform_exists_ = true; + } else { + try { + const auto transform_msg = + tf_buffer_.lookupTransform(base_frame, imu_frame, tf2::TimePointZero); + tf2::convert(transform_msg.transform, tf2_imu_to_base_link); + imu_transform_exists_ = true; + } catch (const tf2::TransformException & ex) { + RCLCPP_WARN(node_->get_logger(), "%s", ex.what()); + RCLCPP_ERROR( + node_->get_logger(), "Please publish TF %s to %s", base_frame.c_str(), imu_frame.c_str()); + + tf2_imu_to_base_link.setOrigin(tf2::Vector3(0.0, 0.0, 0.0)); + tf2_imu_to_base_link.setRotation(tf2::Quaternion(0.0, 0.0, 0.0, 1.0)); + } } - } - geometry_imu_to_base_link_ptr_->transform.rotation = - tf2::toMsg(tf2_imu_to_base_link.getRotation()); -} + geometry_imu_to_base_link_ptr_->transform.rotation = + tf2::toMsg(tf2_imu_to_base_link.getRotation()); + } -template -void DistortionCorrector::enqueueIMU(const sensor_msgs::msg::Imu::ConstSharedPtr imu_msg) -{ - geometry_msgs::msg::Vector3Stamped angular_velocity; - angular_velocity.vector = imu_msg->angular_velocity; - - geometry_msgs::msg::Vector3Stamped transformed_angular_velocity; - tf2::doTransform(angular_velocity, transformed_angular_velocity, *geometry_imu_to_base_link_ptr_); - transformed_angular_velocity.header = imu_msg->header; - angular_velocity_queue_.push_back(transformed_angular_velocity); - - while (!angular_velocity_queue_.empty()) { - // for replay rosbag - if ( - rclcpp::Time(angular_velocity_queue_.front().header.stamp) > - rclcpp::Time(imu_msg->header.stamp)) { - angular_velocity_queue_.pop_front(); - } else if ( // NOLINT - rclcpp::Time(angular_velocity_queue_.front().header.stamp) < - rclcpp::Time(imu_msg->header.stamp) - rclcpp::Duration::from_seconds(1.0)) { - angular_velocity_queue_.pop_front(); - } else { - break; + template + void DistortionCorrector::enqueueIMU(const sensor_msgs::msg::Imu::ConstSharedPtr imu_msg) + { + geometry_msgs::msg::Vector3Stamped angular_velocity; + angular_velocity.vector = imu_msg->angular_velocity; + + geometry_msgs::msg::Vector3Stamped transformed_angular_velocity; + tf2::doTransform( + angular_velocity, transformed_angular_velocity, *geometry_imu_to_base_link_ptr_); + transformed_angular_velocity.header = imu_msg->header; + angular_velocity_queue_.push_back(transformed_angular_velocity); + + while (!angular_velocity_queue_.empty()) { + // for replay rosbag + if ( + rclcpp::Time(angular_velocity_queue_.front().header.stamp) > + rclcpp::Time(imu_msg->header.stamp)) { + angular_velocity_queue_.pop_front(); + } else if ( // NOLINT + rclcpp::Time(angular_velocity_queue_.front().header.stamp) < + rclcpp::Time(imu_msg->header.stamp) - rclcpp::Duration::from_seconds(1.0)) { + angular_velocity_queue_.pop_front(); + } else { + break; + } } } -} -template -void DistortionCorrector::getTwistAndIMUIterator( - bool use_imu, double first_point_time_stamp_sec, - std::deque::iterator & it_twist, - std::deque::iterator & it_imu) -{ - it_twist = std::lower_bound( - std::begin(twist_queue_), std::end(twist_queue_), first_point_time_stamp_sec, - [](const geometry_msgs::msg::TwistStamped & x, const double t) { - return rclcpp::Time(x.header.stamp).seconds() < t; - }); - it_twist = it_twist == std::end(twist_queue_) ? std::end(twist_queue_) - 1 : it_twist; - - if (use_imu && !angular_velocity_queue_.empty()) { - it_imu = std::lower_bound( - std::begin(angular_velocity_queue_), std::end(angular_velocity_queue_), - first_point_time_stamp_sec, [](const geometry_msgs::msg::Vector3Stamped & x, const double t) { + template + void DistortionCorrector::getTwistAndIMUIterator( + bool use_imu, double first_point_time_stamp_sec, + std::deque::iterator & it_twist, + std::deque::iterator & it_imu) + { + it_twist = std::lower_bound( + std::begin(twist_queue_), std::end(twist_queue_), first_point_time_stamp_sec, + [](const geometry_msgs::msg::TwistStamped & x, const double t) { return rclcpp::Time(x.header.stamp).seconds() < t; }); - it_imu = - it_imu == std::end(angular_velocity_queue_) ? std::end(angular_velocity_queue_) - 1 : it_imu; - } -} + it_twist = it_twist == std::end(twist_queue_) ? std::end(twist_queue_) - 1 : it_twist; -template -bool DistortionCorrector::isInputValid(sensor_msgs::msg::PointCloud2 & pointcloud) -{ - if (pointcloud.data.empty() || twist_queue_.empty()) { - RCLCPP_WARN_STREAM_THROTTLE( - node_->get_logger(), *node_->get_clock(), 10000 /* ms */, - "input pointcloud or twist_queue_ is empty."); - return false; + if (use_imu && !angular_velocity_queue_.empty()) { + it_imu = std::lower_bound( + std::begin(angular_velocity_queue_), std::end(angular_velocity_queue_), + first_point_time_stamp_sec, + [](const geometry_msgs::msg::Vector3Stamped & x, const double t) { + return rclcpp::Time(x.header.stamp).seconds() < t; + }); + it_imu = it_imu == std::end(angular_velocity_queue_) ? std::end(angular_velocity_queue_) - 1 + : it_imu; + } } - auto time_stamp_field_it = std::find_if( - std::cbegin(pointcloud.fields), std::cend(pointcloud.fields), - [](const sensor_msgs::msg::PointField & field) { return field.name == "time_stamp"; }); - if (time_stamp_field_it == pointcloud.fields.cend()) { - RCLCPP_WARN_STREAM_THROTTLE( - node_->get_logger(), *node_->get_clock(), 10000 /* ms */, - "Required field time stamp doesn't exist in the point cloud."); - return false; - } + template + bool DistortionCorrector::isInputValid(sensor_msgs::msg::PointCloud2 & pointcloud) + { + if (pointcloud.data.empty() || twist_queue_.empty()) { + RCLCPP_WARN_STREAM_THROTTLE( + node_->get_logger(), *node_->get_clock(), 10000 /* ms */, + "input pointcloud or twist_queue_ is empty."); + return false; + } - if (!utils::is_data_layout_compatible_with_point_xyzircaedt(pointcloud)) { - RCLCPP_ERROR( - node_->get_logger(), - "The pointcloud layout is not compatible with PointXYZIRCAEDT. Aborting"); + auto time_stamp_field_it = std::find_if( + std::cbegin(pointcloud.fields), std::cend(pointcloud.fields), + [](const sensor_msgs::msg::PointField & field) { return field.name == "time_stamp"; }); + if (time_stamp_field_it == pointcloud.fields.cend()) { + RCLCPP_WARN_STREAM_THROTTLE( + node_->get_logger(), *node_->get_clock(), 10000 /* ms */, + "Required field time stamp doesn't exist in the point cloud."); + return false; + } - if (utils::is_data_layout_compatible_with_point_xyziradrt(pointcloud)) { + if (!utils::is_data_layout_compatible_with_point_xyzircaedt(pointcloud)) { RCLCPP_ERROR( node_->get_logger(), - "The pointcloud layout is compatible with PointXYZIRADRT. You may be using legacy " - "code/data"); - } + "The pointcloud layout is not compatible with PointXYZIRCAEDT. Aborting"); - return false; - } + if (utils::is_data_layout_compatible_with_point_xyziradrt(pointcloud)) { + RCLCPP_ERROR( + node_->get_logger(), + "The pointcloud layout is compatible with PointXYZIRADRT. You may be using legacy " + "code/data"); + } - return true; -} + return false; + } -template -void DistortionCorrector::undistortPointCloud( - bool use_imu, sensor_msgs::msg::PointCloud2 & pointcloud) -{ - if (!isInputValid(pointcloud)) return; - - sensor_msgs::PointCloud2Iterator it_x(pointcloud, "x"); - sensor_msgs::PointCloud2Iterator it_y(pointcloud, "y"); - sensor_msgs::PointCloud2Iterator it_z(pointcloud, "z"); - sensor_msgs::PointCloud2ConstIterator it_time_stamp(pointcloud, "time_stamp"); - - double prev_time_stamp_sec{ - pointcloud.header.stamp.sec + 1e-9 * (pointcloud.header.stamp.nanosec + *it_time_stamp)}; - const double first_point_time_stamp_sec{ - pointcloud.header.stamp.sec + 1e-9 * (pointcloud.header.stamp.nanosec + *it_time_stamp)}; - - std::deque::iterator it_twist; - std::deque::iterator it_imu; - getTwistAndIMUIterator(use_imu, first_point_time_stamp_sec, it_twist, it_imu); - - // For performance, do not instantiate `rclcpp::Time` inside of the for-loop - double twist_stamp = rclcpp::Time(it_twist->header.stamp).seconds(); - double imu_stamp{0.0}; - if (use_imu && !angular_velocity_queue_.empty()) { - imu_stamp = rclcpp::Time(it_imu->header.stamp).seconds(); + return true; } - // If there is a point in a pointcloud that cannot be associated, record it to issue a warning - bool is_twist_time_stamp_too_late = false; - bool is_imu_time_stamp_too_late = false; - bool is_twist_valid = true; - bool is_imu_valid = true; - double global_point_stamp; + template + void DistortionCorrector::undistortPointCloud( + bool use_imu, sensor_msgs::msg::PointCloud2 & pointcloud) + { + if (!isInputValid(pointcloud)) return; + + sensor_msgs::PointCloud2Iterator it_x(pointcloud, "x"); + sensor_msgs::PointCloud2Iterator it_y(pointcloud, "y"); + sensor_msgs::PointCloud2Iterator it_z(pointcloud, "z"); + sensor_msgs::PointCloud2ConstIterator it_time_stamp(pointcloud, "time_stamp"); + + double prev_time_stamp_sec{ + pointcloud.header.stamp.sec + 1e-9 * (pointcloud.header.stamp.nanosec + *it_time_stamp)}; + const double first_point_time_stamp_sec{ + pointcloud.header.stamp.sec + 1e-9 * (pointcloud.header.stamp.nanosec + *it_time_stamp)}; + + std::deque::iterator it_twist; + std::deque::iterator it_imu; + getTwistAndIMUIterator(use_imu, first_point_time_stamp_sec, it_twist, it_imu); + + // For performance, do not instantiate `rclcpp::Time` inside of the for-loop + double twist_stamp = rclcpp::Time(it_twist->header.stamp).seconds(); + double imu_stamp{0.0}; + if (use_imu && !angular_velocity_queue_.empty()) { + imu_stamp = rclcpp::Time(it_imu->header.stamp).seconds(); + } - for (; it_x != it_x.end(); ++it_x, ++it_y, ++it_z, ++it_time_stamp) { - is_twist_valid = true; - is_imu_valid = true; + // If there is a point in a pointcloud that cannot be associated, record it to issue a warning + bool is_twist_time_stamp_too_late = false; + bool is_imu_time_stamp_too_late = false; + bool is_twist_valid = true; + bool is_imu_valid = true; + double global_point_stamp; - global_point_stamp = - pointcloud.header.stamp.sec + 1e-9 * (pointcloud.header.stamp.nanosec + *it_time_stamp); + for (; it_x != it_x.end(); ++it_x, ++it_y, ++it_z, ++it_time_stamp) { + is_twist_valid = true; + is_imu_valid = true; - // Get closest twist information - while (it_twist != std::end(twist_queue_) - 1 && global_point_stamp > twist_stamp) { - ++it_twist; - twist_stamp = rclcpp::Time(it_twist->header.stamp).seconds(); - } - if (std::abs(global_point_stamp - twist_stamp) > 0.1) { - is_twist_time_stamp_too_late = true; - is_twist_valid = false; - } + global_point_stamp = + pointcloud.header.stamp.sec + 1e-9 * (pointcloud.header.stamp.nanosec + *it_time_stamp); - // Get closest IMU information - if (use_imu && !angular_velocity_queue_.empty()) { - while (it_imu != std::end(angular_velocity_queue_) - 1 && global_point_stamp > imu_stamp) { - ++it_imu; - imu_stamp = rclcpp::Time(it_imu->header.stamp).seconds(); + // Get closest twist information + while (it_twist != std::end(twist_queue_) - 1 && global_point_stamp > twist_stamp) { + ++it_twist; + twist_stamp = rclcpp::Time(it_twist->header.stamp).seconds(); + } + if (std::abs(global_point_stamp - twist_stamp) > 0.1) { + is_twist_time_stamp_too_late = true; + is_twist_valid = false; } - if (std::abs(global_point_stamp - imu_stamp) > 0.1) { - is_imu_time_stamp_too_late = true; + // Get closest IMU information + if (use_imu && !angular_velocity_queue_.empty()) { + while (it_imu != std::end(angular_velocity_queue_) - 1 && global_point_stamp > imu_stamp) { + ++it_imu; + imu_stamp = rclcpp::Time(it_imu->header.stamp).seconds(); + } + + if (std::abs(global_point_stamp - imu_stamp) > 0.1) { + is_imu_time_stamp_too_late = true; + is_imu_valid = false; + } + } else { is_imu_valid = false; } - } else { - is_imu_valid = false; + + float time_offset = static_cast(global_point_stamp - prev_time_stamp_sec); + + // Undistort a single point based on the strategy + undistortPoint(it_x, it_y, it_z, it_twist, it_imu, time_offset, is_twist_valid, is_imu_valid); + + prev_time_stamp_sec = global_point_stamp; } - float time_offset = static_cast(global_point_stamp - prev_time_stamp_sec); + warnIfTimestampIsTooLate(is_twist_time_stamp_too_late, is_imu_time_stamp_too_late); + } - // Undistort a single point based on the strategy - undistortPoint(it_x, it_y, it_z, it_twist, it_imu, time_offset, is_twist_valid, is_imu_valid); + template + void DistortionCorrector::warnIfTimestampIsTooLate( + bool is_twist_time_stamp_too_late, bool is_imu_time_stamp_too_late) + { + if (is_twist_time_stamp_too_late) { + RCLCPP_WARN_STREAM_THROTTLE( + node_->get_logger(), *node_->get_clock(), 10000 /* ms */, + "Twist time_stamp is too late. Could not interpolate."); + } - prev_time_stamp_sec = global_point_stamp; + if (is_imu_time_stamp_too_late) { + RCLCPP_WARN_STREAM_THROTTLE( + node_->get_logger(), *node_->get_clock(), 10000 /* ms */, + "IMU time_stamp is too late. Could not interpolate."); + } } - warnIfTimestampIsTooLate(is_twist_time_stamp_too_late, is_imu_time_stamp_too_late); -} + ///////////////////////// Functions for different undistortion strategies + //////////////////////////// -template -void DistortionCorrector::warnIfTimestampIsTooLate( - bool is_twist_time_stamp_too_late, bool is_imu_time_stamp_too_late) -{ - if (is_twist_time_stamp_too_late) { - RCLCPP_WARN_STREAM_THROTTLE( - node_->get_logger(), *node_->get_clock(), 10000 /* ms */, - "Twist time_stamp is too late. Could not interpolate."); + void DistortionCorrector2D::initialize() + { + x_ = 0.0f; + y_ = 0.0f; + theta_ = 0.0f; } - if (is_imu_time_stamp_too_late) { - RCLCPP_WARN_STREAM_THROTTLE( - node_->get_logger(), *node_->get_clock(), 10000 /* ms */, - "IMU time_stamp is too late. Could not interpolate."); + void DistortionCorrector3D::initialize() + { + prev_transformation_matrix_ = Eigen::Matrix4f::Identity(); } -} -///////////////////////// Functions for different undistortion strategies ///////////////////////// + void DistortionCorrector2D::setPointCloudTransform( + const std::string & base_frame, const std::string & lidar_frame) + { + if (pointcloud_transform_exists_) { + return; + } -void DistortionCorrector2D::initialize() -{ - x_ = 0.0f; - y_ = 0.0f; - theta_ = 0.0f; -} + if (base_frame == lidar_frame) { + tf2_lidar_to_base_link_.setOrigin(tf2::Vector3(0.0, 0.0, 0.0)); + tf2_lidar_to_base_link_.setRotation(tf2::Quaternion(0.0, 0.0, 0.0, 1.0)); + tf2_base_link_to_lidar_ = tf2_lidar_to_base_link_; + pointcloud_transform_exists_ = true; + } else { + try { + const auto transform_msg = + tf_buffer_.lookupTransform(base_frame, lidar_frame, tf2::TimePointZero); + tf2::convert(transform_msg.transform, tf2_lidar_to_base_link_); + tf2_base_link_to_lidar_ = tf2_lidar_to_base_link_.inverse(); + pointcloud_transform_exists_ = true; + pointcloud_transform_needed_ = true; + } catch (const tf2::TransformException & ex) { + RCLCPP_WARN(node_->get_logger(), "%s", ex.what()); + RCLCPP_ERROR( + node_->get_logger(), "Please publish TF %s to %s", base_frame.c_str(), + lidar_frame.c_str()); + + tf2_lidar_to_base_link_.setOrigin(tf2::Vector3(0.0, 0.0, 0.0)); + tf2_lidar_to_base_link_.setRotation(tf2::Quaternion(0.0, 0.0, 0.0, 1.0)); + tf2_base_link_to_lidar_ = tf2_lidar_to_base_link_; + } + } + } -void DistortionCorrector3D::initialize() -{ - prev_transformation_matrix_ = Eigen::Matrix4f::Identity(); -} + void DistortionCorrector3D::setPointCloudTransform( + const std::string & base_frame, const std::string & lidar_frame) + { + if (pointcloud_transform_exists_) { + return; + } -void DistortionCorrector2D::setPointCloudTransform( - const std::string & base_frame, const std::string & lidar_frame) -{ - if (pointcloud_transform_exists_) { - return; - } + if (base_frame == lidar_frame) { + eigen_lidar_to_base_link_ = Eigen::Matrix4f::Identity(); + eigen_base_link_to_lidar_ = Eigen::Matrix4f::Identity(); + pointcloud_transform_exists_ = true; + } - if (base_frame == lidar_frame) { - tf2_lidar_to_base_link_.setOrigin(tf2::Vector3(0.0, 0.0, 0.0)); - tf2_lidar_to_base_link_.setRotation(tf2::Quaternion(0.0, 0.0, 0.0, 1.0)); - tf2_base_link_to_lidar_ = tf2_lidar_to_base_link_; - pointcloud_transform_exists_ = true; - } else { try { const auto transform_msg = tf_buffer_.lookupTransform(base_frame, lidar_frame, tf2::TimePointZero); - tf2::convert(transform_msg.transform, tf2_lidar_to_base_link_); - tf2_base_link_to_lidar_ = tf2_lidar_to_base_link_.inverse(); + eigen_lidar_to_base_link_ = + tf2::transformToEigen(transform_msg.transform).matrix().cast(); + eigen_base_link_to_lidar_ = eigen_lidar_to_base_link_.inverse(); pointcloud_transform_exists_ = true; pointcloud_transform_needed_ = true; } catch (const tf2::TransformException & ex) { RCLCPP_WARN(node_->get_logger(), "%s", ex.what()); RCLCPP_ERROR( node_->get_logger(), "Please publish TF %s to %s", base_frame.c_str(), lidar_frame.c_str()); - - tf2_lidar_to_base_link_.setOrigin(tf2::Vector3(0.0, 0.0, 0.0)); - tf2_lidar_to_base_link_.setRotation(tf2::Quaternion(0.0, 0.0, 0.0, 1.0)); - tf2_base_link_to_lidar_ = tf2_lidar_to_base_link_; + eigen_lidar_to_base_link_ = Eigen::Matrix4f::Identity(); + eigen_base_link_to_lidar_ = Eigen::Matrix4f::Identity(); } } -} -void DistortionCorrector3D::setPointCloudTransform( - const std::string & base_frame, const std::string & lidar_frame) -{ - if (pointcloud_transform_exists_) { - return; - } - - if (base_frame == lidar_frame) { - eigen_lidar_to_base_link_ = Eigen::Matrix4f::Identity(); - eigen_base_link_to_lidar_ = Eigen::Matrix4f::Identity(); - pointcloud_transform_exists_ = true; - } + inline void DistortionCorrector2D::undistortPointImplementation( + sensor_msgs::PointCloud2Iterator & it_x, sensor_msgs::PointCloud2Iterator & it_y, + sensor_msgs::PointCloud2Iterator & it_z, + std::deque::iterator & it_twist, + std::deque::iterator & it_imu, const float & time_offset, + const bool & is_twist_valid, const bool & is_imu_valid) + { + // Initialize linear velocity and angular velocity + float v{0.0f}, w{0.0f}; + if (is_twist_valid) { + v = static_cast(it_twist->twist.linear.x); + w = static_cast(it_twist->twist.angular.z); + } + if (is_imu_valid) { + w = static_cast(it_imu->vector.z); + } - try { - const auto transform_msg = - tf_buffer_.lookupTransform(base_frame, lidar_frame, tf2::TimePointZero); - eigen_lidar_to_base_link_ = - tf2::transformToEigen(transform_msg.transform).matrix().cast(); - eigen_base_link_to_lidar_ = eigen_lidar_to_base_link_.inverse(); - pointcloud_transform_exists_ = true; - pointcloud_transform_needed_ = true; - } catch (const tf2::TransformException & ex) { - RCLCPP_WARN(node_->get_logger(), "%s", ex.what()); - RCLCPP_ERROR( - node_->get_logger(), "Please publish TF %s to %s", base_frame.c_str(), lidar_frame.c_str()); - eigen_lidar_to_base_link_ = Eigen::Matrix4f::Identity(); - eigen_base_link_to_lidar_ = Eigen::Matrix4f::Identity(); - } -} - -inline void DistortionCorrector2D::undistortPointImplementation( - sensor_msgs::PointCloud2Iterator & it_x, sensor_msgs::PointCloud2Iterator & it_y, - sensor_msgs::PointCloud2Iterator & it_z, - std::deque::iterator & it_twist, - std::deque::iterator & it_imu, const float & time_offset, - const bool & is_twist_valid, const bool & is_imu_valid) -{ - // Initialize linear velocity and angular velocity - float v{0.0f}, w{0.0f}; - if (is_twist_valid) { - v = static_cast(it_twist->twist.linear.x); - w = static_cast(it_twist->twist.angular.z); - } - if (is_imu_valid) { - w = static_cast(it_imu->vector.z); - } + // Undistort point + point_tf_.setValue(*it_x, *it_y, *it_z); - // Undistort point - point_tf_.setValue(*it_x, *it_y, *it_z); + if (pointcloud_transform_needed_) { + point_tf_ = tf2_lidar_to_base_link_ * point_tf_; + } + theta_ += w * time_offset; + baselink_quat_.setValue( + 0, 0, autoware::universe_utils::sin(theta_ * 0.5f), + autoware::universe_utils::cos(theta_ * 0.5f)); // baselink_quat.setRPY(0.0, 0.0, theta); + // (Note that the value is slightly different) + const float dis = v * time_offset; + x_ += dis * autoware::universe_utils::cos(theta_); + y_ += dis * autoware::universe_utils::sin(theta_); + + baselink_tf_odom_.setOrigin(tf2::Vector3(x_, y_, 0.0)); + baselink_tf_odom_.setRotation(baselink_quat_); + + undistorted_point_tf_ = baselink_tf_odom_ * point_tf_; + + if (pointcloud_transform_needed_) { + undistorted_point_tf_ = tf2_base_link_to_lidar_ * undistorted_point_tf_; + } - if (pointcloud_transform_needed_) { - point_tf_ = tf2_lidar_to_base_link_ * point_tf_; - } - theta_ += w * time_offset; - baselink_quat_.setValue( - 0, 0, autoware::universe_utils::sin(theta_ * 0.5f), - autoware::universe_utils::cos( - theta_ * - 0.5f)); // baselink_quat.setRPY(0.0, 0.0, theta); (Note that the value is slightly different) - const float dis = v * time_offset; - x_ += dis * autoware::universe_utils::cos(theta_); - y_ += dis * autoware::universe_utils::sin(theta_); - - baselink_tf_odom_.setOrigin(tf2::Vector3(x_, y_, 0.0)); - baselink_tf_odom_.setRotation(baselink_quat_); - - undistorted_point_tf_ = baselink_tf_odom_ * point_tf_; - - if (pointcloud_transform_needed_) { - undistorted_point_tf_ = tf2_base_link_to_lidar_ * undistorted_point_tf_; + *it_x = static_cast(undistorted_point_tf_.getX()); + *it_y = static_cast(undistorted_point_tf_.getY()); + *it_z = static_cast(undistorted_point_tf_.getZ()); } - *it_x = static_cast(undistorted_point_tf_.getX()); - *it_y = static_cast(undistorted_point_tf_.getY()); - *it_z = static_cast(undistorted_point_tf_.getZ()); -} - -inline void DistortionCorrector3D::undistortPointImplementation( - sensor_msgs::PointCloud2Iterator & it_x, sensor_msgs::PointCloud2Iterator & it_y, - sensor_msgs::PointCloud2Iterator & it_z, - std::deque::iterator & it_twist, - std::deque::iterator & it_imu, const float & time_offset, - const bool & is_twist_valid, const bool & is_imu_valid) -{ - // Initialize linear velocity and angular velocity - float v_x_{0.0f}, v_y_{0.0f}, v_z_{0.0f}, w_x_{0.0f}, w_y_{0.0f}, w_z_{0.0f}; - if (is_twist_valid) { - v_x_ = static_cast(it_twist->twist.linear.x); - v_y_ = static_cast(it_twist->twist.linear.y); - v_z_ = static_cast(it_twist->twist.linear.z); - w_x_ = static_cast(it_twist->twist.angular.x); - w_y_ = static_cast(it_twist->twist.angular.y); - w_z_ = static_cast(it_twist->twist.angular.z); - } - if (is_imu_valid) { - w_x_ = static_cast(it_imu->vector.x); - w_y_ = static_cast(it_imu->vector.y); - w_z_ = static_cast(it_imu->vector.z); - } + inline void DistortionCorrector3D::undistortPointImplementation( + sensor_msgs::PointCloud2Iterator & it_x, sensor_msgs::PointCloud2Iterator & it_y, + sensor_msgs::PointCloud2Iterator & it_z, + std::deque::iterator & it_twist, + std::deque::iterator & it_imu, const float & time_offset, + const bool & is_twist_valid, const bool & is_imu_valid) + { + // Initialize linear velocity and angular velocity + float v_x_{0.0f}, v_y_{0.0f}, v_z_{0.0f}, w_x_{0.0f}, w_y_{0.0f}, w_z_{0.0f}; + if (is_twist_valid) { + v_x_ = static_cast(it_twist->twist.linear.x); + v_y_ = static_cast(it_twist->twist.linear.y); + v_z_ = static_cast(it_twist->twist.linear.z); + w_x_ = static_cast(it_twist->twist.angular.x); + w_y_ = static_cast(it_twist->twist.angular.y); + w_z_ = static_cast(it_twist->twist.angular.z); + } + if (is_imu_valid) { + w_x_ = static_cast(it_imu->vector.x); + w_y_ = static_cast(it_imu->vector.y); + w_z_ = static_cast(it_imu->vector.z); + } - // Undistort point - point_eigen_ << *it_x, *it_y, *it_z, 1.0; - if (pointcloud_transform_needed_) { - point_eigen_ = eigen_lidar_to_base_link_ * point_eigen_; - } + // Undistort point + point_eigen_ << *it_x, *it_y, *it_z, 1.0; + if (pointcloud_transform_needed_) { + point_eigen_ = eigen_lidar_to_base_link_ * point_eigen_; + } - Sophus::SE3f::Tangent twist(v_x_, v_y_, v_z_, w_x_, w_y_, w_z_); - twist = twist * time_offset; - transformation_matrix_ = Sophus::SE3f::exp(twist).matrix(); - transformation_matrix_ = transformation_matrix_ * prev_transformation_matrix_; - undistorted_point_eigen_ = transformation_matrix_ * point_eigen_; + Sophus::SE3f::Tangent twist(v_x_, v_y_, v_z_, w_x_, w_y_, w_z_); + twist = twist * time_offset; + transformation_matrix_ = Sophus::SE3f::exp(twist).matrix(); + transformation_matrix_ = transformation_matrix_ * prev_transformation_matrix_; + undistorted_point_eigen_ = transformation_matrix_ * point_eigen_; - if (pointcloud_transform_needed_) { - undistorted_point_eigen_ = eigen_base_link_to_lidar_ * undistorted_point_eigen_; - } - *it_x = undistorted_point_eigen_[0]; - *it_y = undistorted_point_eigen_[1]; - *it_z = undistorted_point_eigen_[2]; + if (pointcloud_transform_needed_) { + undistorted_point_eigen_ = eigen_base_link_to_lidar_ * undistorted_point_eigen_; + } + *it_x = undistorted_point_eigen_[0]; + *it_y = undistorted_point_eigen_[1]; + *it_z = undistorted_point_eigen_[2]; - prev_transformation_matrix_ = transformation_matrix_; -} + prev_transformation_matrix_ = transformation_matrix_; + } +<<<<<<< HEAD +} // namespace pointcloud_preprocessor +======= } // namespace autoware::pointcloud_preprocessor +>>>>>>> original/main diff --git a/sensing/autoware_pointcloud_preprocessor/src/distortion_corrector/distortion_corrector_node.cpp b/sensing/autoware_pointcloud_preprocessor/src/distortion_corrector/distortion_corrector_node.cpp index 822bb2ba5add5..553ce4a9a27da 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/distortion_corrector/distortion_corrector_node.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/distortion_corrector/distortion_corrector_node.cpp @@ -12,11 +12,18 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "autoware/pointcloud_preprocessor/distortion_corrector/distortion_corrector_node.hpp" +<<<<<<< HEAD +#include "pointcloud_preprocessor/distortion_corrector/distortion_corrector_node.hpp" + +#include "pointcloud_preprocessor/distortion_corrector/distortion_corrector.hpp" +namespace pointcloud_preprocessor +======= #include "autoware/pointcloud_preprocessor/distortion_corrector/distortion_corrector.hpp" +#include "autoware/pointcloud_preprocessor/distortion_corrector/distortion_corrector_node.hpp" namespace autoware::pointcloud_preprocessor +>>>>>>> original/main { /** @brief Constructor. */ DistortionCorrectorComponent::DistortionCorrectorComponent(const rclcpp::NodeOptions & options) @@ -118,7 +125,14 @@ void DistortionCorrectorComponent::onPointCloud(PointCloud2::UniquePtr pointclou } } +<<<<<<< HEAD +} // namespace pointcloud_preprocessor + +#include +RCLCPP_COMPONENTS_REGISTER_NODE(pointcloud_preprocessor::DistortionCorrectorComponent) +======= } // namespace autoware::pointcloud_preprocessor #include RCLCPP_COMPONENTS_REGISTER_NODE(autoware::pointcloud_preprocessor::DistortionCorrectorComponent) +>>>>>>> original/main diff --git a/sensing/autoware_pointcloud_preprocessor/src/downsample_filter/approximate_downsample_filter_nodelet.cpp b/sensing/autoware_pointcloud_preprocessor/src/downsample_filter/approximate_downsample_filter_nodelet.cpp index a4ccca9beea72..bb58f1f001ffd 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/downsample_filter/approximate_downsample_filter_nodelet.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/downsample_filter/approximate_downsample_filter_nodelet.cpp @@ -64,9 +64,9 @@ ApproximateDownsampleFilterComponent::ApproximateDownsampleFilterComponent( : Filter("ApproximateDownsampleFilter", options) { { - voxel_size_x_ = static_cast(declare_parameter("voxel_size_x", 0.3)); - voxel_size_y_ = static_cast(declare_parameter("voxel_size_y", 0.3)); - voxel_size_z_ = static_cast(declare_parameter("voxel_size_z", 0.1)); + voxel_size_x_ = declare_parameter("voxel_size_x"); + voxel_size_y_ = declare_parameter("voxel_size_y"); + voxel_size_z_ = declare_parameter("voxel_size_z"); } using std::placeholders::_1; diff --git a/sensing/autoware_pointcloud_preprocessor/src/downsample_filter/pickup_based_voxel_grid_downsample_filter.cpp b/sensing/autoware_pointcloud_preprocessor/src/downsample_filter/pickup_based_voxel_grid_downsample_filter.cpp index 834f9b8d8fbc0..faf9834e7bc87 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/downsample_filter/pickup_based_voxel_grid_downsample_filter.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/downsample_filter/pickup_based_voxel_grid_downsample_filter.cpp @@ -66,9 +66,9 @@ PickupBasedVoxelGridDownsampleFilterComponent::PickupBasedVoxelGridDownsampleFil } // Initialization of voxel sizes from parameters - voxel_size_x_ = static_cast(declare_parameter("voxel_size_x", 1.0)); - voxel_size_y_ = static_cast(declare_parameter("voxel_size_y", 1.0)); - voxel_size_z_ = static_cast(declare_parameter("voxel_size_z", 1.0)); + voxel_size_x_ = declare_parameter("voxel_size_x"); + voxel_size_y_ = declare_parameter("voxel_size_y"); + voxel_size_z_ = declare_parameter("voxel_size_z"); using std::placeholders::_1; set_param_res_ = this->add_on_set_parameters_callback( diff --git a/sensing/autoware_pointcloud_preprocessor/src/downsample_filter/random_downsample_filter_nodelet.cpp b/sensing/autoware_pointcloud_preprocessor/src/downsample_filter/random_downsample_filter_nodelet.cpp index 32abafb759b73..3e093ce36c269 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/downsample_filter/random_downsample_filter_nodelet.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/downsample_filter/random_downsample_filter_nodelet.cpp @@ -58,7 +58,7 @@ RandomDownsampleFilterComponent::RandomDownsampleFilterComponent( { // set initial parameters { - sample_num_ = static_cast(declare_parameter("sample_num", 1500)); + sample_num_ = declare_parameter("sample_num"); } using std::placeholders::_1; diff --git a/sensing/autoware_pointcloud_preprocessor/src/downsample_filter/voxel_grid_downsample_filter_nodelet.cpp b/sensing/autoware_pointcloud_preprocessor/src/downsample_filter/voxel_grid_downsample_filter_nodelet.cpp index 7f8c500d40f45..b2d5772206a3b 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/downsample_filter/voxel_grid_downsample_filter_nodelet.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/downsample_filter/voxel_grid_downsample_filter_nodelet.cpp @@ -66,9 +66,9 @@ VoxelGridDownsampleFilterComponent::VoxelGridDownsampleFilterComponent( { // set initial parameters { - voxel_size_x_ = static_cast(declare_parameter("voxel_size_x", 0.3)); - voxel_size_y_ = static_cast(declare_parameter("voxel_size_y", 0.3)); - voxel_size_z_ = static_cast(declare_parameter("voxel_size_z", 0.1)); + voxel_size_x_ = declare_parameter("voxel_size_x"); + voxel_size_y_ = declare_parameter("voxel_size_y"); + voxel_size_z_ = declare_parameter("voxel_size_z"); } using std::placeholders::_1; diff --git a/sensing/autoware_pointcloud_preprocessor/src/filter.cpp b/sensing/autoware_pointcloud_preprocessor/src/filter.cpp index 95e85f8450840..f8dfe160ba7ef 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/filter.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/filter.cpp @@ -52,6 +52,7 @@ #include "autoware/pointcloud_preprocessor/filter.hpp" #include "autoware/pointcloud_preprocessor/utility/memory.hpp" +#include "pointcloud_preprocessor/utility/memory.hpp" #include @@ -70,14 +71,14 @@ autoware::pointcloud_preprocessor::Filter::Filter( { // Set parameters (moved from NodeletLazy onInit) { - tf_input_frame_ = static_cast(declare_parameter("input_frame", "")); - tf_output_frame_ = static_cast(declare_parameter("output_frame", "")); - max_queue_size_ = static_cast(declare_parameter("max_queue_size", 5)); + tf_input_frame_ = declare_parameter("input_frame"); + tf_output_frame_ = declare_parameter("output_frame"); + max_queue_size_ = declare_parameter("max_queue_size"); // ---[ Optional parameters - use_indices_ = static_cast(declare_parameter("use_indices", false)); - latched_indices_ = static_cast(declare_parameter("latched_indices", false)); - approximate_sync_ = static_cast(declare_parameter("approximate_sync", false)); + use_indices_ = declare_parameter("use_indices"); + latched_indices_ = declare_parameter("latched_indices"); + approximate_sync_ = declare_parameter("approximate_sync"); RCLCPP_DEBUG_STREAM( this->get_logger(), diff --git a/sensing/autoware_pointcloud_preprocessor/src/outlier_filter/dual_return_outlier_filter_nodelet.cpp b/sensing/autoware_pointcloud_preprocessor/src/outlier_filter/dual_return_outlier_filter_nodelet.cpp index 5fd3262088d83..d9f3b3b8361f5 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/outlier_filter/dual_return_outlier_filter_nodelet.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/outlier_filter/dual_return_outlier_filter_nodelet.cpp @@ -38,29 +38,24 @@ DualReturnOutlierFilterComponent::DualReturnOutlierFilterComponent( { // set initial parameters { - x_max_ = static_cast(declare_parameter("x_max", 18.0)); - x_min_ = static_cast(declare_parameter("x_min", -12.0)); - y_max_ = static_cast(declare_parameter("y_max", 2.0)); - y_min_ = static_cast(declare_parameter("y_min", -2.0)); - z_max_ = static_cast(declare_parameter("z_max", 10.0)); - z_min_ = static_cast(declare_parameter("z_min", 0.0)); - min_azimuth_deg_ = static_cast(declare_parameter("min_azimuth_deg", 135.0)); - max_azimuth_deg_ = static_cast(declare_parameter("max_azimuth_deg", 225.0)); - max_distance_ = static_cast(declare_parameter("max_distance", 12.0)); - vertical_bins_ = static_cast(declare_parameter("vertical_bins", 128)); - max_azimuth_diff_ = static_cast(declare_parameter("max_azimuth_diff", 50.0)); - weak_first_distance_ratio_ = - static_cast(declare_parameter("weak_first_distance_ratio", 1.004)); - general_distance_ratio_ = - static_cast(declare_parameter("general_distance_ratio", 1.03)); - - weak_first_local_noise_threshold_ = - static_cast(declare_parameter("weak_first_local_noise_threshold", 2)); - roi_mode_ = static_cast(declare_parameter("roi_mode", "Fixed_xyz_ROI")); - visibility_error_threshold_ = - static_cast(declare_parameter("visibility_error_threshold", 0.5)); - visibility_warn_threshold_ = - static_cast(declare_parameter("visibility_warn_threshold", 0.7)); + x_max_ = declare_parameter("x_max"); + x_min_ = declare_parameter("x_min"); + y_max_ = declare_parameter("y_max"); + y_min_ = declare_parameter("y_min"); + z_max_ = declare_parameter("z_max"); + z_min_ = declare_parameter("z_min"); + min_azimuth_deg_ = declare_parameter("min_azimuth_deg"); + max_azimuth_deg_ = declare_parameter("max_azimuth_deg"); + max_distance_ = declare_parameter("max_distance"); + vertical_bins_ = declare_parameter("vertical_bins"); + max_azimuth_diff_ = declare_parameter("max_azimuth_diff"); + weak_first_distance_ratio_ = declare_parameter("weak_first_distance_ratio"); + general_distance_ratio_ = declare_parameter("general_distance_ratio"); + + weak_first_local_noise_threshold_ = declare_parameter("weak_first_local_noise_threshold"); + roi_mode_ = declare_parameter("roi_mode"); + visibility_error_threshold_ = declare_parameter("visibility_error_threshold"); + visibility_warn_threshold_ = declare_parameter("visibility_warn_threshold"); } updater_.setHardwareID("dual_return_outlier_filter"); updater_.add( diff --git a/sensing/autoware_pointcloud_preprocessor/src/outlier_filter/radius_search_2d_outlier_filter_nodelet.cpp b/sensing/autoware_pointcloud_preprocessor/src/outlier_filter/radius_search_2d_outlier_filter_nodelet.cpp index 05bf3898d0cf0..6099817a93b26 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/outlier_filter/radius_search_2d_outlier_filter_nodelet.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/outlier_filter/radius_search_2d_outlier_filter_nodelet.cpp @@ -28,8 +28,8 @@ RadiusSearch2DOutlierFilterComponent::RadiusSearch2DOutlierFilterComponent( { // set initial parameters { - min_neighbors_ = static_cast(declare_parameter("min_neighbors", 5)); - search_radius_ = static_cast(declare_parameter("search_radius", 0.2)); + min_neighbors_ = declare_parameter("min_neighbors"); + search_radius_ = declare_parameter("search_radius"); } kd_tree_ = pcl::make_shared>(false); diff --git a/sensing/autoware_pointcloud_preprocessor/src/outlier_filter/ring_outlier_filter_nodelet.cpp b/sensing/autoware_pointcloud_preprocessor/src/outlier_filter/ring_outlier_filter_nodelet.cpp index f8024b7bd116b..23b0f3dc326c5 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/outlier_filter/ring_outlier_filter_nodelet.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/outlier_filter/ring_outlier_filter_nodelet.cpp @@ -47,22 +47,19 @@ RingOutlierFilterComponent::RingOutlierFilterComponent(const rclcpp::NodeOptions // set initial parameters { - distance_ratio_ = static_cast(declare_parameter("distance_ratio", 1.03)); - object_length_threshold_ = - static_cast(declare_parameter("object_length_threshold", 0.1)); - num_points_threshold_ = static_cast(declare_parameter("num_points_threshold", 4)); - max_rings_num_ = static_cast(declare_parameter("max_rings_num", 128)); - max_points_num_per_ring_ = - static_cast(declare_parameter("max_points_num_per_ring", 4000)); - publish_outlier_pointcloud_ = - static_cast(declare_parameter("publish_outlier_pointcloud", false)); - - min_azimuth_deg_ = static_cast(declare_parameter("min_azimuth_deg", 0.0)); - max_azimuth_deg_ = static_cast(declare_parameter("max_azimuth_deg", 360.0)); - max_distance_ = static_cast(declare_parameter("max_distance", 12.0)); - vertical_bins_ = static_cast(declare_parameter("vertical_bins", 128)); - horizontal_bins_ = static_cast(declare_parameter("horizontal_bins", 36)); - noise_threshold_ = static_cast(declare_parameter("noise_threshold", 2)); + distance_ratio_ = declare_parameter("distance_ratio"); + object_length_threshold_ = declare_parameter("object_length_threshold"); + num_points_threshold_ = declare_parameter("num_points_threshold"); + max_rings_num_ = declare_parameter("max_rings_num"); + max_points_num_per_ring_ = declare_parameter("max_points_num_per_ring"); + publish_outlier_pointcloud_ = declare_parameter("publish_outlier_pointcloud"); + + min_azimuth_deg_ = declare_parameter("min_azimuth_deg"); + max_azimuth_deg_ = declare_parameter("max_azimuth_deg"); + max_distance_ = declare_parameter("max_distance"); + vertical_bins_ = declare_parameter("vertical_bins"); + horizontal_bins_ = declare_parameter("horizontal_bins"); + noise_threshold_ = declare_parameter("noise_threshold"); } using std::placeholders::_1; diff --git a/sensing/autoware_pointcloud_preprocessor/src/outlier_filter/voxel_grid_outlier_filter_nodelet.cpp b/sensing/autoware_pointcloud_preprocessor/src/outlier_filter/voxel_grid_outlier_filter_nodelet.cpp index 94dabf7ee5d9b..35ecc5f53435f 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/outlier_filter/voxel_grid_outlier_filter_nodelet.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/outlier_filter/voxel_grid_outlier_filter_nodelet.cpp @@ -28,10 +28,10 @@ VoxelGridOutlierFilterComponent::VoxelGridOutlierFilterComponent( { // set initial parameters { - voxel_size_x_ = static_cast(declare_parameter("voxel_size_x", 0.3)); - voxel_size_y_ = static_cast(declare_parameter("voxel_size_y", 0.3)); - voxel_size_z_ = static_cast(declare_parameter("voxel_size_z", 0.1)); - voxel_points_threshold_ = static_cast(declare_parameter("voxel_points_threshold", 2)); + voxel_size_x_ = declare_parameter("voxel_size_x"); + voxel_size_y_ = declare_parameter("voxel_size_y"); + voxel_size_z_ = declare_parameter("voxel_size_z"); + voxel_points_threshold_ = declare_parameter("voxel_points_threshold"); } using std::placeholders::_1; diff --git a/sensing/autoware_pointcloud_preprocessor/src/passthrough_filter/passthrough_filter_uint16_nodelet.cpp b/sensing/autoware_pointcloud_preprocessor/src/passthrough_filter/passthrough_filter_uint16_nodelet.cpp index cc9f3276033a1..a79bd1986a19a 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/passthrough_filter/passthrough_filter_uint16_nodelet.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/passthrough_filter/passthrough_filter_uint16_nodelet.cpp @@ -29,15 +29,13 @@ PassThroughFilterUInt16Component::PassThroughFilterUInt16Component( { // set initial parameters { - int filter_min = static_cast(declare_parameter("filter_limit_min", 0)); - int filter_max = static_cast(declare_parameter("filter_limit_max", 127)); + int filter_min = declare_parameter("filter_limit_min"); + int filter_max = declare_parameter("filter_limit_max"); impl_.setFilterLimits(filter_min, filter_max); - impl_.setFilterFieldName( - static_cast(declare_parameter("filter_field_name", "ring"))); - impl_.setKeepOrganized(static_cast(declare_parameter("keep_organized", false))); - impl_.setFilterLimitsNegative( - static_cast(declare_parameter("filter_limit_negative", false))); + impl_.setFilterFieldName(declare_parameter("filter_field_name")); + impl_.setKeepOrganized(declare_parameter("keep_organized")); + impl_.setFilterLimitsNegative(declare_parameter("filter_limit_negative")); } using std::placeholders::_1; diff --git a/sensing/autoware_pointcloud_preprocessor/src/pointcloud_accumulator/pointcloud_accumulator_nodelet.cpp b/sensing/autoware_pointcloud_preprocessor/src/pointcloud_accumulator/pointcloud_accumulator_nodelet.cpp index 9502add4b38ce..7e2d911701e6b 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/pointcloud_accumulator/pointcloud_accumulator_nodelet.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/pointcloud_accumulator/pointcloud_accumulator_nodelet.cpp @@ -23,9 +23,8 @@ PointcloudAccumulatorComponent::PointcloudAccumulatorComponent(const rclcpp::Nod { // set initial parameters { - accumulation_time_sec_ = static_cast(declare_parameter("accumulation_time_sec", 2.0)); - pointcloud_buffer_.set_capacity( - static_cast(declare_parameter("pointcloud_buffer_size", 50))); + accumulation_time_sec_ = declare_parameter("accumulation_time_sec"); + pointcloud_buffer_.set_capacity(declare_parameter("pointcloud_buffer_size")); } using std::placeholders::_1; diff --git a/sensing/autoware_pointcloud_preprocessor/src/time_synchronizer/time_synchronizer_nodelet.cpp b/sensing/autoware_pointcloud_preprocessor/src/time_synchronizer/time_synchronizer_nodelet.cpp index edbe30f8f0ea7..b2bb578c3ab0c 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/time_synchronizer/time_synchronizer_nodelet.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/time_synchronizer/time_synchronizer_nodelet.cpp @@ -42,7 +42,7 @@ namespace autoware::pointcloud_preprocessor PointCloudDataSynchronizerComponent::PointCloudDataSynchronizerComponent( const rclcpp::NodeOptions & node_options) : Node("point_cloud_time_synchronizer_component", node_options), - input_twist_topic_type_(declare_parameter("input_twist_topic_type", "twist")) + input_twist_topic_type_(declare_parameter("input_twist_topic_type")) { // initialize debug tool { @@ -57,14 +57,14 @@ PointCloudDataSynchronizerComponent::PointCloudDataSynchronizerComponent( // Set parameters std::string synchronized_pointcloud_postfix; { - output_frame_ = static_cast(declare_parameter("output_frame", "")); + output_frame_ = declare_parameter("output_frame"); keep_input_frame_in_synchronized_pointcloud_ = - static_cast(declare_parameter("keep_input_frame_in_synchronized_pointcloud", false)); + declare_parameter("keep_input_frame_in_synchronized_pointcloud"); if (output_frame_.empty() && !keep_input_frame_in_synchronized_pointcloud_) { RCLCPP_ERROR(get_logger(), "Need an 'output_frame' parameter to be set before continuing!"); return; } - declare_parameter("input_topics", std::vector()); + declare_parameter>("input_topics"); input_topics_ = get_parameter("input_topics").as_string_array(); if (input_topics_.empty()) { RCLCPP_ERROR(get_logger(), "Need a 'input_topics' parameter to be set before continuing!"); @@ -76,13 +76,13 @@ PointCloudDataSynchronizerComponent::PointCloudDataSynchronizerComponent( } // output topic name postfix synchronized_pointcloud_postfix = - declare_parameter("synchronized_pointcloud_postfix", "pointcloud"); + declare_parameter("synchronized_pointcloud_postfix"); // Optional parameters - maximum_queue_size_ = static_cast(declare_parameter("max_queue_size", 5)); - timeout_sec_ = static_cast(declare_parameter("timeout_sec", 0.1)); + maximum_queue_size_ = declare_parameter("max_queue_size"); + timeout_sec_ = declare_parameter("timeout_sec"); - input_offset_ = declare_parameter("input_offset", std::vector{}); + input_offset_ = declare_parameter>("input_offset"); // If input_offset_ is not defined, set all offsets to 0 if (input_offset_.empty()) { diff --git a/sensing/autoware_pointcloud_preprocessor/src/utility/geometry.cpp b/sensing/autoware_pointcloud_preprocessor/src/utility/geometry.cpp index 7e9604c3dd305..33cb8be49009b 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/utility/geometry.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/utility/geometry.cpp @@ -12,9 +12,15 @@ // See the License for the specific language governing permissions and // limitations under the License. +<<<<<<< HEAD +#include "pointcloud_preprocessor/utility/geometry.hpp" + +namespace pointcloud_preprocessor::utils +======= #include "autoware/pointcloud_preprocessor/utility/geometry.hpp" namespace autoware::pointcloud_preprocessor::utils +>>>>>>> original/main { void to_cgal_polygon(const geometry_msgs::msg::Polygon & polygon_in, PolygonCgal & polygon_out) { @@ -155,4 +161,8 @@ bool point_within_cgal_polys( return false; } +<<<<<<< HEAD +} // namespace pointcloud_preprocessor::utils +======= } // namespace autoware::pointcloud_preprocessor::utils +>>>>>>> original/main diff --git a/sensing/autoware_pointcloud_preprocessor/src/utility/memory.cpp b/sensing/autoware_pointcloud_preprocessor/src/utility/memory.cpp index 7e265bcfb4960..e7ebdbb8a8347 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/utility/memory.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/utility/memory.cpp @@ -12,11 +12,19 @@ // See the License for the specific language governing permissions and // limitations under the License. +<<<<<<< HEAD +#include "pointcloud_preprocessor/utility/memory.hpp" + +#include + +namespace pointcloud_preprocessor::utils +======= #include "autoware/pointcloud_preprocessor/utility/memory.hpp" #include namespace autoware::pointcloud_preprocessor::utils +>>>>>>> original/main { bool is_data_layout_compatible_with_point_xyzi(const sensor_msgs::msg::PointCloud2 & input) { @@ -208,4 +216,8 @@ bool is_data_layout_compatible_with_point_xyzircaedt(const sensor_msgs::msg::Poi return same_layout; } +<<<<<<< HEAD +} // namespace pointcloud_preprocessor::utils +======= } // namespace autoware::pointcloud_preprocessor::utils +>>>>>>> original/main diff --git a/sensing/autoware_pointcloud_preprocessor/src/vector_map_filter/lanelet2_map_filter_nodelet.cpp b/sensing/autoware_pointcloud_preprocessor/src/vector_map_filter/lanelet2_map_filter_nodelet.cpp index b6de8bb87d5b1..35230db6a4ea3 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/vector_map_filter/lanelet2_map_filter_nodelet.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/vector_map_filter/lanelet2_map_filter_nodelet.cpp @@ -39,8 +39,8 @@ Lanelet2MapFilterComponent::Lanelet2MapFilterComponent(const rclcpp::NodeOptions // Set parameters { - voxel_size_x_ = declare_parameter("voxel_size_x", 0.04); - voxel_size_y_ = declare_parameter("voxel_size_y", 0.04); + voxel_size_x_ = declare_parameter("voxel_size_x"); + voxel_size_y_ = declare_parameter("voxel_size_y"); } // Set publisher diff --git a/sensing/autoware_pointcloud_preprocessor/src/vector_map_filter/vector_map_inside_area_filter.cpp b/sensing/autoware_pointcloud_preprocessor/src/vector_map_filter/vector_map_inside_area_filter.cpp index 6272fe30e47c0..3c0d5ce3c69b5 100644 --- a/sensing/autoware_pointcloud_preprocessor/src/vector_map_filter/vector_map_inside_area_filter.cpp +++ b/sensing/autoware_pointcloud_preprocessor/src/vector_map_filter/vector_map_inside_area_filter.cpp @@ -67,8 +67,7 @@ VectorMapInsideAreaFilterComponent::VectorMapInsideAreaFilterComponent( const rclcpp::NodeOptions & node_options) : Filter("VectorMapInsideAreaFilter", node_options) { - polygon_type_ = - static_cast(declare_parameter("polygon_type", "no_obstacle_segmentation_area")); + polygon_type_ = declare_parameter("polygon_type"); using std::placeholders::_1; // Set subscriber @@ -77,8 +76,8 @@ VectorMapInsideAreaFilterComponent::VectorMapInsideAreaFilterComponent( std::bind(&VectorMapInsideAreaFilterComponent::mapCallback, this, _1)); // Set parameters - use_z_filter_ = declare_parameter("use_z_filter", false); - z_threshold_ = declare_parameter("z_threshold", 0.0f); // defined in the base_link frame + use_z_filter_ = declare_parameter("use_z_filter"); + z_threshold_ = declare_parameter("z_threshold"); // defined in the base_link frame } void VectorMapInsideAreaFilterComponent::filter( diff --git a/sensing/autoware_pointcloud_preprocessor/test/test_distortion_corrector_node.cpp b/sensing/autoware_pointcloud_preprocessor/test/test_distortion_corrector_node.cpp index f90314a001105..1e83aceba7dfe 100644 --- a/sensing/autoware_pointcloud_preprocessor/test/test_distortion_corrector_node.cpp +++ b/sensing/autoware_pointcloud_preprocessor/test/test_distortion_corrector_node.cpp @@ -25,8 +25,13 @@ // imu (6msgs): // 10.09, 10.117, 10.144, 10.171, 10.198, 10.225 +<<<<<<< HEAD +#include "autoware/universe_utils/math/trigonometry.hpp" +#include "pointcloud_preprocessor/distortion_corrector/distortion_corrector.hpp" +======= #include "autoware/pointcloud_preprocessor/distortion_corrector/distortion_corrector.hpp" #include "autoware/universe_utils/math/trigonometry.hpp" + >>>>>>> original/main #include @@ -41,16 +46,22 @@ #include -class DistortionCorrectorTest : public ::testing::Test + class DistortionCorrectorTest : public ::testing::Test { protected: void SetUp() override { node_ = std::make_shared("test_node"); distortion_corrector_2d_ = +<<<<<<< HEAD + std::make_shared(node_.get()); + distortion_corrector_3d_ = + std::make_shared(node_.get()); +======= std::make_shared(node_.get()); distortion_corrector_3d_ = std::make_shared(node_.get()); +>>>>>>> original/main // Setup TF tf_broadcaster_ = std::make_shared(node_); @@ -246,10 +257,15 @@ class DistortionCorrectorTest : public ::testing::Test } std::shared_ptr node_; +<<<<<<< HEAD + std::shared_ptr distortion_corrector_2d_; + std::shared_ptr distortion_corrector_3d_; +======= std::shared_ptr distortion_corrector_2d_; std::shared_ptr distortion_corrector_3d_; +>>>>>>> original/main std::shared_ptr tf_broadcaster_; static constexpr float standard_tolerance_{1e-4}; diff --git a/sensing/autoware_pointcloud_preprocessor/test/test_utilities.cpp b/sensing/autoware_pointcloud_preprocessor/test/test_utilities.cpp index f99901896dfb6..4ddc97351fe1f 100644 --- a/sensing/autoware_pointcloud_preprocessor/test/test_utilities.cpp +++ b/sensing/autoware_pointcloud_preprocessor/test/test_utilities.cpp @@ -12,7 +12,11 @@ // See the License for the specific language governing permissions and // limitations under the License. +<<<<<<< HEAD +#include "pointcloud_preprocessor/utility/geometry.hpp" +======= #include "autoware/pointcloud_preprocessor/utility/geometry.hpp" + >>>>>>> original/main #include @@ -25,7 +29,7 @@ #include -constexpr double EPSILON = 1e-6; + constexpr double EPSILON = 1e-6; class RemovePolygonCgalFromCloudTest : public ::testing::Test { diff --git a/sensing/radar_scan_to_pointcloud2/CMakeLists.txt b/sensing/radar_scan_to_pointcloud2/CMakeLists.txt new file mode 100644 index 0000000000000..44b668b49c87a --- /dev/null +++ b/sensing/radar_scan_to_pointcloud2/CMakeLists.txt @@ -0,0 +1,40 @@ +cmake_minimum_required(VERSION 3.5) +project(radar_scan_to_pointcloud2) + +# Dependencies +find_package(autoware_cmake REQUIRED) +autoware_package() + +find_package(PCL REQUIRED COMPONENTS common) + +# Targets +ament_auto_add_library(${PROJECT_NAME} SHARED + src/radar_scan_to_pointcloud2_node.cpp +) + +target_include_directories(${PROJECT_NAME} + SYSTEM PUBLIC ${PCL_INCLUDE_DIRS} +) +target_link_libraries(${PROJECT_NAME} + ${PCL_LIBRARIES} +) + +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "autoware::radar_scan_to_pointcloud2::RadarScanToPointcloud2Node" + EXECUTABLE radar_scan_to_pointcloud2_node +) + +# Tests +if(BUILD_TESTING) + list(APPEND AMENT_LINT_AUTO_EXCLUDE ament_cmake_uncrustify) + + find_package(ament_lint_auto REQUIRED) + ament_lint_auto_find_test_dependencies() +endif() + +# Package +ament_auto_package( + INSTALL_TO_SHARE + launch + config +) diff --git a/sensing/radar_scan_to_pointcloud2/src/radar_scan_to_pointcloud2_node.cpp b/sensing/radar_scan_to_pointcloud2/src/radar_scan_to_pointcloud2_node.cpp new file mode 100644 index 0000000000000..30f267eebc205 --- /dev/null +++ b/sensing/radar_scan_to_pointcloud2/src/radar_scan_to_pointcloud2_node.cpp @@ -0,0 +1,159 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "radar_scan_to_pointcloud2_node.hpp" + +#include +#include +#include +#include + +#include +#include + +namespace +{ +template +bool update_param( + const std::vector & params, const std::string & name, T & value) +{ + const auto itr = std::find_if( + params.cbegin(), params.cend(), + [&name](const rclcpp::Parameter & p) { return p.get_name() == name; }); + // Not found + if (itr == params.cend()) { + return false; + } + value = itr->template get_value(); + return true; +} + +pcl::PointXYZI getPointXYZI(const radar_msgs::msg::RadarReturn & radar, float intensity) +{ + pcl::PointXYZI point; + const float r_xy = radar.range * std::cos(radar.elevation); + point.x = r_xy * std::cos(radar.azimuth); + point.y = r_xy * std::sin(radar.azimuth); + point.z = radar.range * std::sin(radar.elevation); + point.intensity = intensity; + return point; +} + +pcl::PointCloud toAmplitudePCL(const radar_msgs::msg::RadarScan & radar_scan) +{ + pcl::PointCloud pcl; + for (const auto & radar : radar_scan.returns) { + pcl.push_back(getPointXYZI(radar, radar.amplitude)); + } + return pcl; +} + +sensor_msgs::msg::PointCloud2 toAmplitudePointcloud2(const radar_msgs::msg::RadarScan & radar_scan) +{ + sensor_msgs::msg::PointCloud2 pointcloud_msg; + auto pcl_pointcloud = toAmplitudePCL(radar_scan); + pcl::toROSMsg(pcl_pointcloud, pointcloud_msg); + pointcloud_msg.header = radar_scan.header; + return pointcloud_msg; +} + +pcl::PointCloud toDopplerPCL(const radar_msgs::msg::RadarScan & radar_scan) +{ + pcl::PointCloud pcl; + for (const auto & radar : radar_scan.returns) { + pcl.push_back(getPointXYZI(radar, radar.doppler_velocity)); + } + return pcl; +} + +sensor_msgs::msg::PointCloud2 toDopplerPointcloud2(const radar_msgs::msg::RadarScan & radar_scan) +{ + sensor_msgs::msg::PointCloud2 pointcloud_msg; + auto pcl_pointcloud = toDopplerPCL(radar_scan); + pcl::toROSMsg(pcl_pointcloud, pointcloud_msg); + pointcloud_msg.header = radar_scan.header; + return pointcloud_msg; +} +} // namespace + +namespace autoware::radar_scan_to_pointcloud2 +{ +using radar_msgs::msg::RadarReturn; +using radar_msgs::msg::RadarScan; +using sensor_msgs::msg::PointCloud2; + +RadarScanToPointcloud2Node::RadarScanToPointcloud2Node(const rclcpp::NodeOptions & node_options) +: Node("radar_scan_to_pointcloud2", node_options) +{ + using std::placeholders::_1; + + // Parameter Server + set_param_res_ = this->add_on_set_parameters_callback( + std::bind(&RadarScanToPointcloud2Node::onSetParam, this, _1)); + + // Node Parameter + node_param_.publish_amplitude_pointcloud = + declare_parameter("publish_amplitude_pointcloud"); + node_param_.publish_doppler_pointcloud = declare_parameter("publish_doppler_pointcloud"); + + // Subscriber + sub_radar_ = create_subscription( + "~/input/radar", rclcpp::QoS{1}, std::bind(&RadarScanToPointcloud2Node::onData, this, _1)); + + { + rclcpp::PublisherOptions pub_options; + pub_options.qos_overriding_options = rclcpp::QosOverridingOptions::with_default_policies(); + // Publisher + pub_amplitude_pointcloud_ = + create_publisher("~/output/amplitude_pointcloud", 1, pub_options); + pub_doppler_pointcloud_ = + create_publisher("~/output/doppler_pointcloud", 1, pub_options); + } +} + +rcl_interfaces::msg::SetParametersResult RadarScanToPointcloud2Node::onSetParam( + const std::vector & params) +{ + rcl_interfaces::msg::SetParametersResult result; + + try { + auto & p = node_param_; + update_param(params, "publish_amplitude_pointcloud", p.publish_amplitude_pointcloud); + update_param(params, "publish_doppler_pointcloud", p.publish_doppler_pointcloud); + } catch (const rclcpp::exceptions::InvalidParameterTypeException & e) { + result.successful = false; + result.reason = e.what(); + return result; + } + result.successful = true; + result.reason = "success"; + return result; +} + +void RadarScanToPointcloud2Node::onData(const RadarScan::ConstSharedPtr radar_msg) +{ + if (node_param_.publish_amplitude_pointcloud) { + amplitude_pointcloud = toAmplitudePointcloud2(*radar_msg); + pub_amplitude_pointcloud_->publish(amplitude_pointcloud); + } + if (node_param_.publish_doppler_pointcloud) { + doppler_pointcloud = toDopplerPointcloud2(*radar_msg); + pub_doppler_pointcloud_->publish(doppler_pointcloud); + } +} + +} // namespace autoware::radar_scan_to_pointcloud2 + +#include "rclcpp_components/register_node_macro.hpp" +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::radar_scan_to_pointcloud2::RadarScanToPointcloud2Node) diff --git a/sensing/radar_scan_to_pointcloud2/src/radar_scan_to_pointcloud2_node.hpp b/sensing/radar_scan_to_pointcloud2/src/radar_scan_to_pointcloud2_node.hpp new file mode 100644 index 0000000000000..f758d149b184b --- /dev/null +++ b/sensing/radar_scan_to_pointcloud2/src/radar_scan_to_pointcloud2_node.hpp @@ -0,0 +1,70 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef RADAR_SCAN_TO_POINTCLOUD2_NODE_HPP_ +#define RADAR_SCAN_TO_POINTCLOUD2_NODE_HPP_ + +#include "rclcpp/rclcpp.hpp" + +#include +#include + +#include +#include +#include + +namespace autoware::radar_scan_to_pointcloud2 +{ +using radar_msgs::msg::RadarReturn; +using radar_msgs::msg::RadarScan; +using sensor_msgs::msg::PointCloud2; + +class RadarScanToPointcloud2Node : public rclcpp::Node +{ +public: + explicit RadarScanToPointcloud2Node(const rclcpp::NodeOptions & node_options); + + struct NodeParam + { + bool publish_amplitude_pointcloud{}; + bool publish_doppler_pointcloud{}; + }; + +private: + // Subscriber + rclcpp::Subscription::SharedPtr sub_radar_{}; + + // Callback + void onData(const RadarScan::ConstSharedPtr radar_msg); + + // Publisher + rclcpp::Publisher::SharedPtr pub_amplitude_pointcloud_{}; + rclcpp::Publisher::SharedPtr pub_doppler_pointcloud_{}; + + // Parameter Server + OnSetParametersCallbackHandle::SharedPtr set_param_res_; + rcl_interfaces::msg::SetParametersResult onSetParam( + const std::vector & params); + + // Data buffer + sensor_msgs::msg::PointCloud2 amplitude_pointcloud; + sensor_msgs::msg::PointCloud2 doppler_pointcloud; + + // Parameter + NodeParam node_param_{}; +}; + +} // namespace autoware::radar_scan_to_pointcloud2 + +#endif // RADAR_SCAN_TO_POINTCLOUD2_NODE_HPP_ diff --git a/sensing/radar_static_pointcloud_filter/src/radar_static_pointcloud_filter_node.cpp b/sensing/radar_static_pointcloud_filter/src/radar_static_pointcloud_filter_node.cpp new file mode 100644 index 0000000000000..74c099b9bf32b --- /dev/null +++ b/sensing/radar_static_pointcloud_filter/src/radar_static_pointcloud_filter_node.cpp @@ -0,0 +1,174 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "radar_static_pointcloud_filter_node.hpp" + +#include +#include +#include +#include + +#include +#include +#include + +namespace +{ +template +bool update_param( + const std::vector & params, const std::string & name, T & value) +{ + const auto itr = std::find_if( + params.cbegin(), params.cend(), + [&name](const rclcpp::Parameter & p) { return p.get_name() == name; }); + // Not found + if (itr == params.cend()) { + return false; + } + value = itr->template get_value(); + return true; +} + +geometry_msgs::msg::Vector3 getVelocity(const radar_msgs::msg::RadarReturn & radar) +{ + return geometry_msgs::build() + .x(radar.doppler_velocity * std::cos(radar.azimuth)) + .y(radar.doppler_velocity * std::sin(radar.azimuth)) + .z(0.0); +} + +geometry_msgs::msg::Vector3 getTransformedVelocity( + const geometry_msgs::msg::Vector3 velocity, + geometry_msgs::msg::TransformStamped::ConstSharedPtr transform) +{ + geometry_msgs::msg::Vector3Stamped velocity_stamped{}; + velocity_stamped.vector = velocity; + geometry_msgs::msg::Vector3Stamped transformed_velocity_stamped{}; + tf2::doTransform(velocity_stamped, transformed_velocity_stamped, *transform); + return velocity_stamped.vector; +} + +geometry_msgs::msg::Vector3 compensateEgoVehicleTwist( + const radar_msgs::msg::RadarReturn & radar, + const geometry_msgs::msg::TwistWithCovariance & ego_vehicle_twist_with_covariance, + geometry_msgs::msg::TransformStamped::ConstSharedPtr transform) +{ + const geometry_msgs::msg::Vector3 radar_velocity = getVelocity(radar); + const geometry_msgs::msg::Vector3 v_r = getTransformedVelocity(radar_velocity, transform); + + const auto v_e = ego_vehicle_twist_with_covariance.twist.linear; + return geometry_msgs::build() + .x(v_r.x + v_e.x) + .y(v_r.y + v_e.y) + .z(v_r.z + v_e.z); +} +} // namespace + +namespace autoware::radar_static_pointcloud_filter +{ +using nav_msgs::msg::Odometry; +using radar_msgs::msg::RadarReturn; +using radar_msgs::msg::RadarScan; + +RadarStaticPointcloudFilterNode::RadarStaticPointcloudFilterNode( + const rclcpp::NodeOptions & node_options) +: Node("radar_static_pointcloud_filter", node_options) +{ + using std::placeholders::_1; + using std::placeholders::_2; + + // Parameter Server + set_param_res_ = this->add_on_set_parameters_callback( + std::bind(&RadarStaticPointcloudFilterNode::onSetParam, this, _1)); + + // Node Parameter + node_param_.doppler_velocity_sd = declare_parameter("doppler_velocity_sd"); + + // Subscriber + transform_listener_ = std::make_shared(this); + + sub_radar_.subscribe(this, "~/input/radar", rclcpp::QoS{1}.get_rmw_qos_profile()); + sub_odometry_.subscribe(this, "~/input/odometry", rclcpp::QoS{1}.get_rmw_qos_profile()); + + sync_ptr_ = std::make_shared(SyncPolicy(10), sub_radar_, sub_odometry_); + sync_ptr_->registerCallback(std::bind(&RadarStaticPointcloudFilterNode::onData, this, _1, _2)); + + // Publisher + pub_static_radar_ = create_publisher("~/output/static_radar_scan", 1); + pub_dynamic_radar_ = create_publisher("~/output/dynamic_radar_scan", 1); +} + +rcl_interfaces::msg::SetParametersResult RadarStaticPointcloudFilterNode::onSetParam( + const std::vector & params) +{ + rcl_interfaces::msg::SetParametersResult result; + try { + auto & p = node_param_; + update_param(params, "doppler_velocity_sd", p.doppler_velocity_sd); + } catch (const rclcpp::exceptions::InvalidParameterTypeException & e) { + result.successful = false; + result.reason = e.what(); + return result; + } + result.successful = true; + result.reason = "success"; + return result; +} + +void RadarStaticPointcloudFilterNode::onData( + const RadarScan::ConstSharedPtr radar_msg, const Odometry::ConstSharedPtr odom_msg) +{ + geometry_msgs::msg::TransformStamped::ConstSharedPtr transform; + + try { + transform = transform_listener_->getTransform( + odom_msg->header.frame_id, radar_msg->header.frame_id, odom_msg->header.stamp, + rclcpp::Duration::from_seconds(0.2)); + } catch (tf2::TransformException & ex) { + RCLCPP_INFO(this->get_logger(), "Could not transform"); + return; + } + + RadarScan static_radar{}; + RadarScan dynamic_radar{}; + static_radar.header = radar_msg->header; + dynamic_radar.header = radar_msg->header; + + for (const auto & radar_return : radar_msg->returns) { + if (isStaticPointcloud(radar_return, odom_msg, transform)) { + static_radar.returns.emplace_back(radar_return); + } else { + dynamic_radar.returns.emplace_back(radar_return); + } + } + pub_static_radar_->publish(static_radar); + pub_dynamic_radar_->publish(dynamic_radar); +} + +bool RadarStaticPointcloudFilterNode::isStaticPointcloud( + const RadarReturn & radar_return, const Odometry::ConstSharedPtr & odom_msg, + geometry_msgs::msg::TransformStamped::ConstSharedPtr transform) +{ + geometry_msgs::msg::Vector3 compensated_velocity = + compensateEgoVehicleTwist(radar_return, odom_msg->twist, transform); + + return (-node_param_.doppler_velocity_sd < compensated_velocity.x) && + (compensated_velocity.x < node_param_.doppler_velocity_sd); +} + +} // namespace autoware::radar_static_pointcloud_filter + +#include "rclcpp_components/register_node_macro.hpp" +RCLCPP_COMPONENTS_REGISTER_NODE( + autoware::radar_static_pointcloud_filter::RadarStaticPointcloudFilterNode) diff --git a/sensing/radar_static_pointcloud_filter/src/radar_static_pointcloud_filter_node.hpp b/sensing/radar_static_pointcloud_filter/src/radar_static_pointcloud_filter_node.hpp new file mode 100644 index 0000000000000..eff8437eb51d2 --- /dev/null +++ b/sensing/radar_static_pointcloud_filter/src/radar_static_pointcloud_filter_node.hpp @@ -0,0 +1,82 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef RADAR_STATIC_POINTCLOUD_FILTER_NODE_HPP_ +#define RADAR_STATIC_POINTCLOUD_FILTER_NODE_HPP_ + +#include "autoware/universe_utils/ros/transform_listener.hpp" + +#include + +#include +#include + +#include +#include +#include + +#include +#include +#include +#include + +namespace autoware::radar_static_pointcloud_filter +{ +using nav_msgs::msg::Odometry; +using radar_msgs::msg::RadarReturn; +using radar_msgs::msg::RadarScan; + +class RadarStaticPointcloudFilterNode : public rclcpp::Node +{ +public: + explicit RadarStaticPointcloudFilterNode(const rclcpp::NodeOptions & node_options); + + struct NodeParam + { + double doppler_velocity_sd{}; + }; + +private: + // Subscriber + message_filters::Subscriber sub_radar_{}; + message_filters::Subscriber sub_odometry_{}; + std::shared_ptr transform_listener_; + + using SyncPolicy = message_filters::sync_policies::ApproximateTime; + using Sync = message_filters::Synchronizer; + typename std::shared_ptr sync_ptr_; + + // Callback + void onData(const RadarScan::ConstSharedPtr radar_msg, const Odometry::ConstSharedPtr odom_msg); + + // Publisher + rclcpp::Publisher::SharedPtr pub_static_radar_{}; + rclcpp::Publisher::SharedPtr pub_dynamic_radar_{}; + + // Parameter Server + OnSetParametersCallbackHandle::SharedPtr set_param_res_; + rcl_interfaces::msg::SetParametersResult onSetParam( + const std::vector & params); + + // Parameter + NodeParam node_param_{}; + + // Function + bool isStaticPointcloud( + const RadarReturn & radar_return, const Odometry::ConstSharedPtr & odom_msg, + geometry_msgs::msg::TransformStamped::ConstSharedPtr transform); +}; +} // namespace autoware::radar_static_pointcloud_filter + +#endif // RADAR_STATIC_POINTCLOUD_FILTER_NODE_HPP_ diff --git a/sensing/radar_threshold_filter/src/radar_threshold_filter_node.cpp b/sensing/radar_threshold_filter/src/radar_threshold_filter_node.cpp new file mode 100644 index 0000000000000..7a260766c2c7a --- /dev/null +++ b/sensing/radar_threshold_filter/src/radar_threshold_filter_node.cpp @@ -0,0 +1,161 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "radar_threshold_filter_node.hpp" + +#include + +#include +#include +#include + +namespace +{ +template +bool update_param( + const std::vector & params, const std::string & name, T & value) +{ + const auto itr = std::find_if( + params.cbegin(), params.cend(), + [&name](const rclcpp::Parameter & p) { return p.get_name() == name; }); + + // Not found + if (itr == params.cend()) { + return false; + } + + value = itr->template get_value(); + return true; +} + +bool isWithin(double value, double max, double min) +{ + if (min < value && value < max) { + return true; + } else { + return false; + } +} +} // namespace + +namespace autoware::radar_threshold_filter +{ +using radar_msgs::msg::RadarReturn; +using radar_msgs::msg::RadarScan; +using std::placeholders::_1; + +RadarThresholdFilterNode::RadarThresholdFilterNode(const rclcpp::NodeOptions & node_options) +: Node("radar_threshold_filter", node_options) +{ + // Parameter Server + set_param_res_ = this->add_on_set_parameters_callback( + std::bind(&RadarThresholdFilterNode::onSetParam, this, _1)); + + // Node Parameter + node_param_.is_amplitude_filter = declare_parameter("node_params.is_amplitude_filter"); + node_param_.amplitude_min = declare_parameter("node_params.amplitude_min"); + node_param_.amplitude_max = declare_parameter("node_params.amplitude_max"); + node_param_.is_range_filter = declare_parameter("node_params.is_range_filter"); + node_param_.range_min = declare_parameter("node_params.range_min"); + node_param_.range_max = declare_parameter("node_params.range_max"); + node_param_.is_azimuth_filter = declare_parameter("node_params.is_azimuth_filter"); + node_param_.azimuth_min = declare_parameter("node_params.azimuth_min"); + node_param_.azimuth_max = declare_parameter("node_params.azimuth_max"); + node_param_.is_z_filter = declare_parameter("node_params.is_z_filter"); + node_param_.z_min = declare_parameter("node_params.z_min"); + node_param_.z_max = declare_parameter("node_params.z_max"); + + // Subscriber + sub_radar_ = create_subscription( + "~/input/radar", rclcpp::QoS{1}, std::bind(&RadarThresholdFilterNode::onData, this, _1)); + + // Publisher + pub_radar_ = create_publisher("~/output/radar", 1); +} + +rcl_interfaces::msg::SetParametersResult RadarThresholdFilterNode::onSetParam( + const std::vector & params) +{ + rcl_interfaces::msg::SetParametersResult result; + try { + { + auto & p = node_param_; + update_param(params, "node_params.is_amplitude_filter", p.is_amplitude_filter); + update_param(params, "node_params.amplitude_min", p.amplitude_min); + update_param(params, "node_params.amplitude_max", p.amplitude_max); + update_param(params, "node_params.is_range_filter", p.is_range_filter); + update_param(params, "node_params.range_min", p.range_min); + update_param(params, "node_params.range_max", p.range_max); + update_param(params, "node_params.is_azimuth_filter", p.is_azimuth_filter); + update_param(params, "node_params.azimuth_min", p.azimuth_min); + update_param(params, "node_params.azimuth_max", p.azimuth_max); + update_param(params, "node_params.is_z_filter", p.is_z_filter); + update_param(params, "node_params.z_min", p.z_min); + update_param(params, "node_params.z_max", p.z_max); + } + } catch (const rclcpp::exceptions::InvalidParameterTypeException & e) { + result.successful = false; + result.reason = e.what(); + return result; + } + result.successful = true; + result.reason = "success"; + return result; +} + +void RadarThresholdFilterNode::onData(const RadarScan::ConstSharedPtr radar_msg) +{ + RadarScan output; + output.header = radar_msg->header; + for (const auto & radar_return : radar_msg->returns) { + if (isWithinThreshold(radar_return)) { + output.returns.push_back(radar_return); + } + } + pub_radar_->publish(output); +} + +bool RadarThresholdFilterNode::isWithinThreshold(const RadarReturn & radar_return) +{ + if ( + node_param_.is_amplitude_filter && + !isWithin(radar_return.amplitude, node_param_.amplitude_max, node_param_.amplitude_min)) { + return false; + } + + if ( + node_param_.is_range_filter && + !isWithin(radar_return.range, node_param_.range_max, node_param_.range_min)) { + return false; + } + + if ( + node_param_.is_azimuth_filter && + !isWithin(radar_return.azimuth, node_param_.azimuth_max, node_param_.azimuth_min)) { + return false; + } + + if (node_param_.is_z_filter) { + const auto z = radar_return.range * std::sin(radar_return.elevation); + if (!isWithin(z, node_param_.z_max, node_param_.z_min)) { + return false; + } + } + return true; +} + +} // namespace autoware::radar_threshold_filter + +#include "rclcpp_components/register_node_macro.hpp" +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::radar_threshold_filter::RadarThresholdFilterNode) diff --git a/sensing/radar_threshold_filter/src/radar_threshold_filter_node.hpp b/sensing/radar_threshold_filter/src/radar_threshold_filter_node.hpp new file mode 100644 index 0000000000000..f503638a4d01e --- /dev/null +++ b/sensing/radar_threshold_filter/src/radar_threshold_filter_node.hpp @@ -0,0 +1,78 @@ +// Copyright 2022 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef RADAR_THRESHOLD_FILTER_NODE_HPP_ +#define RADAR_THRESHOLD_FILTER_NODE_HPP_ + +#include + +#include + +#include +#include +#include +#include + +namespace autoware::radar_threshold_filter +{ +using radar_msgs::msg::RadarReturn; +using radar_msgs::msg::RadarScan; + +class RadarThresholdFilterNode : public rclcpp::Node +{ +public: + explicit RadarThresholdFilterNode(const rclcpp::NodeOptions & node_options); + + struct NodeParam + { + bool is_amplitude_filter{}; + double amplitude_min{}; + double amplitude_max{}; + bool is_range_filter{}; + double range_min{}; + double range_max{}; + bool is_azimuth_filter{}; + double azimuth_min{}; + double azimuth_max{}; + bool is_z_filter{}; + double z_min{}; + double z_max{}; + }; + +private: + // Subscriber + rclcpp::Subscription::SharedPtr sub_radar_{}; + + // Callback + void onData(const RadarScan::ConstSharedPtr radar_msg); + + // Publisher + rclcpp::Publisher::SharedPtr pub_radar_{}; + + // Parameter Server + OnSetParametersCallbackHandle::SharedPtr set_param_res_; + rcl_interfaces::msg::SetParametersResult onSetParam( + const std::vector & params); + + // Parameter + NodeParam node_param_{}; + +public: + // Function + bool isWithinThreshold(const RadarReturn & radar_return); +}; + +} // namespace autoware::radar_threshold_filter + +#endif // RADAR_THRESHOLD_FILTER_NODE_HPP_