From 21f5749ffb72d330f20dac96b62e62b96226eb77 Mon Sep 17 00:00:00 2001 From: "Yi-Hsiang Fang (Vivid)" <146902905+vividf@users.noreply.github.com> Date: Wed, 24 Apr 2024 16:14:34 +0900 Subject: [PATCH 1/7] =?UTF-8?q?marker=20radar=20lidar=20tutorial=20done,?= =?UTF-8?q?=20still=20need=20to=20adjust=20the=20image=20posi=E2=80=A6=20(?= =?UTF-8?q?#168)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit marker radar lidar tutorial tutorial done, still need to adjust the image position Signed-off-by: vividf --- .../marker_radar_lidar_calibrator/add1.jpg | Bin 0 -> 87941 bytes .../marker_radar_lidar_calibrator/add2.jpg | Bin 0 -> 103919 bytes .../marker_radar_lidar_calibrator/delete1.jpg | Bin 0 -> 262331 bytes .../marker_radar_lidar_calibrator/delete2.jpg | Bin 0 -> 216893 bytes .../end_calibration1.jpg | Bin 0 -> 282926 bytes .../end_calibration2.jpg | Bin 0 -> 281068 bytes .../marker_radar_lidar_calibrator/menu1.jpg | Bin 0 -> 11028 bytes 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z0i%64kWZ?`F_ckFoj#I|%OarqK5SMi-UIcp5j6eOq03A;{3Vl;5*_yxCWzOe-J-`p z$Dng?$UIugbYBBN9ZHylq>-TnB}0jV`T`IH`ae)&mZqlRlT|mg^G3X9y2Nr*!ycPB zOndpN8tlGN$ZLK9W3S+Z?3{|f5Tg7GA=Fy}0xm*T-gjd)m=-vMPfYI7f8RYt{+#SX>$JfxD8_lqn*bh2KTMJ-tgXod;;e$vrF>ANfqeqgY3 z%FO#Aw&@v4W0ckKt_bm-#`*zM3fEwoF#4NF`=fxx&SUBBxz(vYAN4_{!aVV49 zWsKu{hL%Dn&3Huhe~K~Qu?%G|*Qg&Y^;VsvnJ6a4I%mA(7-P&^XXkv#;c8s$^u+b? zdxHvW_4|Q8r$iw3TE1WR^pO6FyXIug^~nvxqu|XNR1PM|-)lH`5|gClw?F;!&N*P7 zQX9kEWF9rVIo4;{9ayxrF@M?{n0vOFET6i+ux*vw$big&R_@Q?Jr z?x#Qb9H%tiAmx#RAuQJK#3;aXGK=9^05PhSuP!Y>9d#XZ0b_asALF87Tg6`Z_Y4%L TO0YK@LE8av5rmp;I-mL + + scene0 + scene1 + + +

Before deletion.

+

After deletion.

+ + + +### Metric plotter + +The tool also provides a metric plotter for real time visualization shown in below. + +![marker_radar_lidar_calibrator](../images/marker_radar_lidar_calibrator/metric_plotter1.jpg) + +The subplots at the top display the cross-validation errors, while the bottom subplot shows the average errors in the calibration procedure. Plotting for the average errors begins after three pairs have been collected. For the cross-validation errors, plotting starts after four pairs have been collected. + +Consider the left-top subplot, which plots the cross-validation errors for distance, as an example of how these errors are calculated. When the x-axis value is 3, it indicates that we estimate the transformation using 3 samples from the 5 converged tracks. We then calculate the distance errors using the remaining 2 samples. This process is repeated for 5 choose 3 (5C3) times, which totals 10 times, and the errors are then averaged. he light blue area represents the variance of the 10 calculated distance errors. + +### Send calibration + +User can click the `Send calibration` button once they are satisfied. It is recommended that user collect more pairs to increase the accuarcy. Therefore, in this tutorial, we will add all of the pairs in the rosbag. Additional, user can also stop the calibration when the line in the cross validation error is converged. -First press the extract background model to extract the background +Once the `Send calibration` button are clicked, the result will be sent to the sensor calibration manager. No pairs can be added or deleted afterward like the image shown below. Please make sure you want to end the calibration process when you click the button. -Afterward, when the lidar and the radar detection show up, press the add lidar-radar pair to add them for calibration, + + + + + + + + + +
marker_radar_lidar_calibratormarker_radar_lidar_calibrator

Rosbag ended.

After clicking send calibration.

-After you add more than three lidar-radar pairs, the metric plotter will show the average calibration error. After the pairs are more than four, the cross-validation error will also show on the plotter with an additional std error. +## Results -During the calibration, if you add some pairs that are not stable or mismatched, you can click the delete the previous pair to delete them +After the calibration process is finished, the sensor_calibration_manager will display the results in the tf tree and allow user to save the calibration data to a file. +![marker_radar_lidar_calibrator](../images/marker_radar_lidar_calibrator/menu4.jpg) -Finally, when the cross-validation error is converged, you can press the send calibration to stop the calibration and then click the save calibration to save the calibration result in yaml +To evaluate the calibration result, user can measure the calibrated radar points (green) are closer than the initial radar points (red) to the lidar points. diff --git a/sensor/docs/tutorials/tag_based_pnp_calibrator.md b/sensor/docs/tutorials/tag_based_pnp_calibrator.md index 80a184a9..fb9a9697 100644 --- a/sensor/docs/tutorials/tag_based_pnp_calibrator.md +++ b/sensor/docs/tutorials/tag_based_pnp_calibrator.md @@ -11,7 +11,7 @@ Installation instructions can be found [here](../../README.md) ## Data preparation -Please download the data (rosbag) from [here](https://drive.google.com/drive/folders/1gFrrchW9mWM1huWMYuJ0nWB2n1BfCJag?usp=drive_link). +Please download the data (rosbag) from [here](https://drive.google.com/drive/folders/1gFrrchW9mWM1huWMYuJ0nWB2n1BfCJag). The rosabg includes four different topics including `camera_info`, `image_rect_color/compressed`, `pointcloud_raw`, and `/tf_static`. @@ -23,7 +23,7 @@ The required space for calibration depends on the vehicle and sensors used. For ### Apriltag -Apriltag are the only moving elements during the calibration process and must detected by both cameras and lidars. +Apriltag are the only moving elements during the calibration process and must detected by both camera and lidar. Depending on the lidar model and the available space, the required Apriltag size may vary, but so far we have had good results with 0.6m and 0.8m tags (the provided sizes correspond to an edge's size. In these cases the payloads are 0.45m and 0.6m). In addition to the Apriltag size, which determines the physical positions in which a tag can be detected, it is of equal importance the structure in which the Apriltag is mounted. Depending on the structure's shape and size, it may interfere with the lidar detection algorithm, so it is recommended to prepare a mount that holds the tag in a way that is not visible to the sensor (see the provided example). diff --git a/sensor/marker_radar_lidar_calibrator/scripts/metrics_plotter_node.py b/sensor/marker_radar_lidar_calibrator/scripts/metrics_plotter_node.py index b3f4f0dc..1c3b0200 100755 --- a/sensor/marker_radar_lidar_calibrator/scripts/metrics_plotter_node.py +++ b/sensor/marker_radar_lidar_calibrator/scripts/metrics_plotter_node.py @@ -71,6 +71,10 @@ def plot_label_and_set_xy_lim(self): self.subplot3.set_xlabel("number of tracks") self.subplot3.set_ylabel("yaw error [deg]") + max_ylim0 = ( + max(self.crossval_distance_error_list) if self.crossval_distance_error_list else 5 + ) + max_ylim1 = max(self.crossval_yaw_error_list) if self.crossval_yaw_error_list else 1 max_ylim2 = ( max(self.calibration_distance_error_list) if self.calibration_distance_error_list else 5 ) @@ -92,6 +96,8 @@ def plot_label_and_set_xy_lim(self): self.subplot2.set_xlim(2.9, avg_xlim + 0.3) self.subplot3.set_xlim(2.9, avg_xlim + 0.3) + self.subplot0.set_ylim(0, max_ylim0 + 5) + self.subplot1.set_ylim(0, max_ylim1 + 0.1) self.subplot2.set_ylim(0, max_ylim2 + 5) self.subplot3.set_ylim(0, max_ylim3 + 0.1) From 2932d6320d09673e222b25458d9025dafcd86a32 Mon Sep 17 00:00:00 2001 From: vividf Date: Wed, 24 Apr 2024 18:50:56 +0900 Subject: [PATCH 2/7] change size of image Signed-off-by: vividf --- .../tutorials/mapping_based_calibrator.md | 23 ++++++++++----- .../marker_radar_lidar_calibrator.md | 29 +++++++++++++------ .../tutorials/tag_based_pnp_calibrator.md | 28 +++++++++++++----- 3 files changed, 56 insertions(+), 24 deletions(-) diff --git a/sensor/docs/tutorials/mapping_based_calibrator.md b/sensor/docs/tutorials/mapping_based_calibrator.md index ba29b957..a2662332 100644 --- a/sensor/docs/tutorials/mapping_based_calibrator.md +++ b/sensor/docs/tutorials/mapping_based_calibrator.md @@ -36,18 +36,20 @@ ros2 run sensor_calibration_manager sensor_calibration_manager In `project`, select `rdv`, and in `calibrator`, select `mapping_based_calibrator`. Then, press `Continue`. -![segment](../images/mapping_based_calibrator/menu1.jpg) +
+ menu1 +
A menu titled `Launcher configuration` should appear in the UI, and the user may change any parameter he deems convenient. However, for this tutorial, we will use the default values. After configuring the parameters, click `Launch`. -![segment](../images/mapping_based_calibrator/menu2.jpg) +![mapping_based_calibrator](../images/mapping_based_calibrator/menu2.jpg) The following UI should be displayed. When the `Calibrate` button becomes available, click it. If it does not become available, it means that either the required `tf` or services are not available. In this tutorial, since the `tf` are published by the provided rosbags, run the rag (`ros2 bag play lidar_lidar.db3 --clock -r 0.1`) first and launch the tools afterward to trigger the `Calibrate` button. -![segment](../images/mapping_based_calibrator/menu3.jpg) +![mapping_based_calibrator](../images/mapping_based_calibrator/menu3.jpg) Note: In the default values in the `/calibration_tools/sensor/sensor_calibration_manager/launch/rdv/mapping_based_lidar_lidar_calibrator.launch.xml`, the RDV vehicle set the top_lidar as `mapping lidar`, and other lidars as `calibration lidars`. @@ -55,7 +57,7 @@ Note: In the default values in the `/calibration_tools/sensor/sensor_calibration Once you have clicked the `Calibrate` button, the first step of calibration process will automatically start building the map by using NDT/GICP algorithm with the `mapping lidar`. You can visualize process of building the map on the `rviz`. -![segment](../images/mapping_based_calibrator/map1.jpg) +![mapping_based_calibrator](../images/mapping_based_calibrator/map1.jpg) You can also see the log in the console showing that the map is building. @@ -73,7 +75,7 @@ You can also see the log in the console showing that the map is building. When the roabag is finished playing, you should see the pointcloud map and the path of the lidar frames like the picture below. -![segment](../images/mapping_based_calibrator/map2.jpg) +![mapping_based_calibrator](../images/mapping_based_calibrator/map2.jpg) ## Calibration @@ -108,15 +110,20 @@ Once the calibration process is completed, the displayed text should be as follo User can also see the three different colors of pointcloud in the `rviz`. white for the map from the `mapping lidar`, red for the initial map from the `calibration lidars`, and green for the calibrated map from the `calibration lidars`. -![segment](../images/mapping_based_calibrator/map3.jpg) +![mapping_based_calibrator](../images/mapping_based_calibrator/map3.jpg) ## Results After the calibration process is finished, the sensor_calibration_manager will display the results in the tf tree and allow user to save the calibration data to a file. -![segment](../images/mapping_based_calibrator/menu4.jpg) + +
+ menu4 +
To assess the calibration results, users can precisely measure static objects within the point cloud map, such as stationary vehicles, traffic cones, and walls. The image below displays the vehicle within the pointcloud, allowing for a comparison of results before and after calibration. It is evident that the initial point cloud from `calibration lidars` (shown in red) has been successfully calibrated (shown in green) and is now aligned with the `mapping lidar` (shown in white). -![segment](../images/mapping_based_calibrator/vehicle_calibrated.jpg) +
+ vehicle_calibrated +
diff --git a/sensor/docs/tutorials/marker_radar_lidar_calibrator.md b/sensor/docs/tutorials/marker_radar_lidar_calibrator.md index b6dac399..39625d2d 100644 --- a/sensor/docs/tutorials/marker_radar_lidar_calibrator.md +++ b/sensor/docs/tutorials/marker_radar_lidar_calibrator.md @@ -54,15 +54,21 @@ In this tutorial, since the `tf` are published by the provided rosbags, run the Once the user starts running the tutorial rosbag, the point cloud will appear in `rviz` as shown in the example below. Press the `Extract Background Model button` in the UI to begin extracting the background. -![marker_radar_lidar_calibrator](../images/marker_radar_lidar_calibrator/rviz1.jpg) +
+ rviz1 +
Once user click the button, it will show like the image below. -![marker_radar_lidar_calibrator](../images/marker_radar_lidar_calibrator/rviz2.jpg) +
+ rviz2 +
Once the background is extracted, it will show like the image below. User can see that there are the `Add lidar-radar pair` button is enabled. -![sensor/docs/tutorials/marker_radar_lidar_calibrator.md](../images/marker_radar_lidar_calibrator/rviz3.jpg) +
+ rviz3 +
Also, following text should be shown in the console. @@ -76,7 +82,9 @@ After the background model has been extracted, the user can carry the radar refl Also, the green points represent the lidar foreground points, while the purple points indicate radar foreground detections. The blue point is the estimated center of the radar reflector derived from the lidar point cloud. -![marker_radar_lidar_calibrator](../images/marker_radar_lidar_calibrator/add1.jpg) +
+ add1 +
When the purple line connects the purple point (the radar estimation of the reflector) and the blue point (the lidar estimation of the reflector), the user can press the `Add lidar-radar pair` button to register them as a pair. @@ -90,8 +98,8 @@ Using the tutorial rosbag as an example, we can delete the latest radar and lida - - + + @@ -117,8 +125,8 @@ Once the `Send calibration` button are clicked, the result will be sent to the s
scene0scene1delete1delete2

Before deletion.

- - + + @@ -129,6 +137,9 @@ Once the `Send calibration` button are clicked, the result will be sent to the s ## Results After the calibration process is finished, the sensor_calibration_manager will display the results in the tf tree and allow user to save the calibration data to a file. -![marker_radar_lidar_calibrator](../images/marker_radar_lidar_calibrator/menu4.jpg) + +
+ menu4 +
To evaluate the calibration result, user can measure the calibrated radar points (green) are closer than the initial radar points (red) to the lidar points. diff --git a/sensor/docs/tutorials/tag_based_pnp_calibrator.md b/sensor/docs/tutorials/tag_based_pnp_calibrator.md index fb9a9697..d221d75e 100644 --- a/sensor/docs/tutorials/tag_based_pnp_calibrator.md +++ b/sensor/docs/tutorials/tag_based_pnp_calibrator.md @@ -39,12 +39,14 @@ ros2 run sensor_calibration_manager sensor_calibration_manager In `project`, select `x2`, and in `calibrator`, select `tag_based_pnp_calibrator`. Then, press `Continue`. -![segment](../images/tag_based_pnp_calibrator/menu1.jpg) +
+ menu1 +
A menu titled `Launcher configuration` should appear in the UI, and the user may change any parameter he deems convenient. For this tutorial, we will modify the default values `calibration_pairs` from `9` to `8` as the bag have 8 Apriltag detection and also modify the `camera_name` from `camera0` to `camera6`. After configuring the parameters, click `Launch`. -![segment](../images/tag_based_pnp_calibrator/menu2.jpg) +![tag_based_pnp_calibrator](../images/tag_based_pnp_calibrator/menu2.jpg) The following UI should be displayed. When the `Calibrate` button becomes available, click it. If it does not become available, it means that either the required `tf` or services are not available. @@ -74,16 +76,28 @@ In the end of the calibration, we can get 8 detection pairs which shown as below ## Results After the calibration process is finished, the sensor_calibration_manager will display the results in the tf tree and allow user to save the calibration data to a file. -![segment](../images/tag_based_pnp_calibrator/menu4.jpg) + +
+ menu4 +
User can modify the `visualization options` in the right side of the `image view`. To compare the results easier, user can set the `Marker size (m)` to `0.04` and set the `PC subsample factor` to `1`. -![segment](../images/tag_based_pnp_calibrator/visualization_bar.jpg) +
+ visualization_bar +
After setting the options above, change the `/initial_tf` (in the `visualization options`) to `/current_tf`. By doing this, user can easily measure the difference after the calibration. The images below show that with the calibrated tranformation, the projected pointcloud align better with the imaage. -![segment](../images/tag_based_pnp_calibrator/init_tf.jpg) - -![segment](../images/tag_based_pnp_calibrator/calibrated_tf.jpg) +
marker_radar_lidar_calibratormarker_radar_lidar_calibratorend_calibration1end_calibration2

Rosbag ended.

+ + + + + + + + +
init_tfcalibrated_tf

Before Calibration.

After Calibration.

From a42e59c4c5a92d3168bfa7d1bce8eb2f32d9982c Mon Sep 17 00:00:00 2001 From: vividf Date: Wed, 24 Apr 2024 18:55:49 +0900 Subject: [PATCH 3/7] modify image Signed-off-by: vividf --- .../marker_radar_lidar_calibrator.md | 20 +++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/sensor/docs/tutorials/marker_radar_lidar_calibrator.md b/sensor/docs/tutorials/marker_radar_lidar_calibrator.md index 39625d2d..569793c8 100644 --- a/sensor/docs/tutorials/marker_radar_lidar_calibrator.md +++ b/sensor/docs/tutorials/marker_radar_lidar_calibrator.md @@ -54,21 +54,21 @@ In this tutorial, since the `tf` are published by the provided rosbags, run the Once the user starts running the tutorial rosbag, the point cloud will appear in `rviz` as shown in the example below. Press the `Extract Background Model button` in the UI to begin extracting the background. -
+

rviz1 -

+

Once user click the button, it will show like the image below. -
+

rviz2 -

+

Once the background is extracted, it will show like the image below. User can see that there are the `Add lidar-radar pair` button is enabled. -
+

rviz3 -

+

Also, following text should be shown in the console. @@ -82,9 +82,9 @@ After the background model has been extracted, the user can carry the radar refl Also, the green points represent the lidar foreground points, while the purple points indicate radar foreground detections. The blue point is the estimated center of the radar reflector derived from the lidar point cloud. -
+

add1 -

+

When the purple line connects the purple point (the radar estimation of the reflector) and the blue point (the lidar estimation of the reflector), the user can press the `Add lidar-radar pair` button to register them as a pair. @@ -138,8 +138,8 @@ Once the `Send calibration` button are clicked, the result will be sent to the s After the calibration process is finished, the sensor_calibration_manager will display the results in the tf tree and allow user to save the calibration data to a file. -
+

menu4 -

+

To evaluate the calibration result, user can measure the calibrated radar points (green) are closer than the initial radar points (red) to the lidar points. From e2b6faea6638423c0e3a465aa751e244aa2b1bfd Mon Sep 17 00:00:00 2001 From: vividf Date: Wed, 24 Apr 2024 18:58:36 +0900 Subject: [PATCH 4/7] modify image for pnp and mapping Signed-off-by: vividf --- sensor/docs/tutorials/mapping_based_calibrator.md | 12 ++++++------ sensor/docs/tutorials/tag_based_pnp_calibrator.md | 12 ++++++------ 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/sensor/docs/tutorials/mapping_based_calibrator.md b/sensor/docs/tutorials/mapping_based_calibrator.md index a2662332..5411a81c 100644 --- a/sensor/docs/tutorials/mapping_based_calibrator.md +++ b/sensor/docs/tutorials/mapping_based_calibrator.md @@ -36,9 +36,9 @@ ros2 run sensor_calibration_manager sensor_calibration_manager In `project`, select `rdv`, and in `calibrator`, select `mapping_based_calibrator`. Then, press `Continue`. -
+

menu1 -

+

A menu titled `Launcher configuration` should appear in the UI, and the user may change any parameter he deems convenient. However, for this tutorial, we will use the default values. After configuring the parameters, click `Launch`. @@ -116,14 +116,14 @@ User can also see the three different colors of pointcloud in the `rviz`. white After the calibration process is finished, the sensor_calibration_manager will display the results in the tf tree and allow user to save the calibration data to a file. -
+

menu4 -

+

To assess the calibration results, users can precisely measure static objects within the point cloud map, such as stationary vehicles, traffic cones, and walls. The image below displays the vehicle within the pointcloud, allowing for a comparison of results before and after calibration. It is evident that the initial point cloud from `calibration lidars` (shown in red) has been successfully calibrated (shown in green) and is now aligned with the `mapping lidar` (shown in white). -
+

vehicle_calibrated -

+

diff --git a/sensor/docs/tutorials/tag_based_pnp_calibrator.md b/sensor/docs/tutorials/tag_based_pnp_calibrator.md index d221d75e..35678013 100644 --- a/sensor/docs/tutorials/tag_based_pnp_calibrator.md +++ b/sensor/docs/tutorials/tag_based_pnp_calibrator.md @@ -39,9 +39,9 @@ ros2 run sensor_calibration_manager sensor_calibration_manager In `project`, select `x2`, and in `calibrator`, select `tag_based_pnp_calibrator`. Then, press `Continue`. -
+

menu1 -

+

A menu titled `Launcher configuration` should appear in the UI, and the user may change any parameter he deems convenient. For this tutorial, we will modify the default values `calibration_pairs` from `9` to `8` as the bag have 8 Apriltag detection and also modify the `camera_name` from `camera0` to `camera6`. After configuring the parameters, click `Launch`. @@ -77,15 +77,15 @@ In the end of the calibration, we can get 8 detection pairs which shown as below After the calibration process is finished, the sensor_calibration_manager will display the results in the tf tree and allow user to save the calibration data to a file. -
+

menu4 -

+

User can modify the `visualization options` in the right side of the `image view`. To compare the results easier, user can set the `Marker size (m)` to `0.04` and set the `PC subsample factor` to `1`. -
+

visualization_bar -

+

After setting the options above, change the `/initial_tf` (in the `visualization options`) to `/current_tf`. By doing this, user can easily measure the difference after the calibration. From 4ff7a581f5d4f665c417d69696367598c6fe004a Mon Sep 17 00:00:00 2001 From: vividf Date: Wed, 24 Apr 2024 19:38:03 +0900 Subject: [PATCH 5/7] fix grammer errors Signed-off-by: vividf --- .../tutorials/mapping_based_calibrator.md | 12 +++---- .../tutorials/tag_based_pnp_calibrator.md | 20 +++++------ sensor/mapping_based_calibrator/README.md | 22 ++++++------ .../marker_radar_lidar_calibrator/README.md | 18 +++++----- sensor/tag_based_pnp_calibrator/README.md | 36 +++++++++++-------- 5 files changed, 58 insertions(+), 50 deletions(-) diff --git a/sensor/docs/tutorials/mapping_based_calibrator.md b/sensor/docs/tutorials/mapping_based_calibrator.md index 5411a81c..335bf80d 100644 --- a/sensor/docs/tutorials/mapping_based_calibrator.md +++ b/sensor/docs/tutorials/mapping_based_calibrator.md @@ -23,7 +23,7 @@ The required space for calibration depends on the vehicle and sensors used. For ### Vehicle -When doing the calibration, user needs to drive the vehicle in order to collect the pointcloud for buliding map. While recording the data during the experiment, slow down the vehicle speed as munch as possible. For instance, drive slower than 5 km/hr is a good speed for recording a good data. Also during the experiment, try to avoid people walking around the vehicle, try to make to surrounding static. +When doing the calibration, the user needs to drive the vehicle to collect the point cloud for building the map. While recording data during the experiment, slow down the vehicle's speed as much as possible. For instance, driving slower than 5 km/hr is good for recording quality data. Also, during the experiment, try to avoid people walking around the vehicle and aim to keep the surroundings static. ## Launching the tool @@ -73,15 +73,15 @@ You can also see the log in the console showing that the map is building. [mapping_based_calibrator-1] [calibration_mapper]: New frame (id=28 | kid=-1). Distance=2.26 Delta_distance0.11 Delta_time0.10. Unprocessed=0 Frames=29 Keyframes=3 ``` -When the roabag is finished playing, you should see the pointcloud map and the path of the lidar frames like the picture below. +When the rosbag has finished playing, you should see the point cloud map and the path of the lidar frames, as shown in the picture below. ![mapping_based_calibrator](../images/mapping_based_calibrator/map2.jpg) ## Calibration -Calibration starts anytime when the user send the command `ros2 service call /stop_mapping std_srvs/srv/Empty`. User can also send this command before the rosbag ended if user think that the data collection is enough for calibration. +Calibration starts anytime when the user sends the command `ros2 service call /stop_mapping std_srvs/srv/Empty`. User can also send this command before the rosbag ends if they think the data collected is sufficient for calibration. -In this tutorial, we send the command after the rosbag run until the end. Once the command is sent, the displayed text should be as follows: +In this tutorial, we send the command after the rosbag runs until the end. Once the command is sent, the displayed text should be as follows: ```bash [mapping_based_calibrator-1] [mapping_based_calibrator_node]: Mapper stopped through service (operator()()) @@ -89,9 +89,9 @@ In this tutorial, we send the command after the rosbag run until the end. Once t [mapping_based_calibrator-1] [mapping_based_calibrator_node]: Beginning lidar calibration for pandar_front (operator()()) ``` -The calibration process may take some time as it involves multiple lidars. Users should remain patient and monitor the console output to follow the calibration progress. +The calibration process may take some time, as it involves multiple lidars. Users should remain patient and monitor the console output to track the progress of the calibration. -Once the calibration process is completed, the displayed text should be as follows: +Once the calibration process is complete, the displayed text should be as follows: ```bash [mapping_based_calibrator-1] [lidar_calibrator(pandar_left)]: Calibration result as a tf main lidar -> lidar_calibrator(pandar_left) diff --git a/sensor/docs/tutorials/tag_based_pnp_calibrator.md b/sensor/docs/tutorials/tag_based_pnp_calibrator.md index 35678013..e2c9fecd 100644 --- a/sensor/docs/tutorials/tag_based_pnp_calibrator.md +++ b/sensor/docs/tutorials/tag_based_pnp_calibrator.md @@ -21,12 +21,12 @@ The rosabg includes four different topics including `camera_info`, `image_rect_c The required space for calibration depends on the vehicle and sensors used. For a normal consumer-level car, a space of `5m x 10m` should be sufficient. -### Apriltag +### AprilTag (LidarTag) -Apriltag are the only moving elements during the calibration process and must detected by both camera and lidar. -Depending on the lidar model and the available space, the required Apriltag size may vary, but so far we have had good results with 0.6m and 0.8m tags (the provided sizes correspond to an edge's size. In these cases the payloads are 0.45m and 0.6m). +AprilTag are the only moving elements during the calibration process and must detected by both camera and lidar. +Depending on the lidar model and the available space, the required AprilTag size may vary, but so far we have had good results with 0.6m and 0.8m tags (the provided sizes correspond to an edge's size. In these cases the payloads are 0.45m and 0.6m). -In addition to the Apriltag size, which determines the physical positions in which a tag can be detected, it is of equal importance the structure in which the Apriltag is mounted. Depending on the structure's shape and size, it may interfere with the lidar detection algorithm, so it is recommended to prepare a mount that holds the tag in a way that is not visible to the sensor (see the provided example). +In addition to the AprilTag size, which determines the physical positions in which a tag can be detected, it is of equal importance the structure in which the AprilTag is mounted. Depending on the structure's shape and size, it may interfere with the lidar detection algorithm, so it is recommended to prepare a mount that holds the tag in a way that is not visible to the sensor (see the provided example). ## Launching the tool @@ -44,7 +44,7 @@ In `project`, select `x2`, and in `calibrator`, select `tag_based_pnp_calibrator

A menu titled `Launcher configuration` should appear in the UI, and the user may change any parameter he deems convenient. -For this tutorial, we will modify the default values `calibration_pairs` from `9` to `8` as the bag have 8 Apriltag detection and also modify the `camera_name` from `camera0` to `camera6`. After configuring the parameters, click `Launch`. +For this tutorial, we will modify the default values `calibration_pairs` from `9` to `8` as the bag have 8 AprilTag detections and also modify the `camera_name` from `camera0` to `camera6`. After configuring the parameters, click `Launch`. ![tag_based_pnp_calibrator](../images/tag_based_pnp_calibrator/menu2.jpg) @@ -57,13 +57,13 @@ In this tutorial, since the `tf` are published by the provided rosbags, run the ## Calibration -The calibration start automatically after click the `Calibrate` button. It will keep calibrate the lidartag detection and apriltag detection until the number of the detection fit the user defined `calibratino_pairs` in the `Launcher configuration`. +The calibration start automatically after click the `Calibrate` button. It will keep calibrate the LidarTag detections and AprilTag detections until the number of the detections fit the user defined `calibration_pairs` in the `Launcher configuration`. When user start the calibration, `rviz` and the `image view` should be displayed like below. ![segment](../images/tag_based_pnp_calibrator/visualization1.jpg) -After the tools detect the lidartag and apriltag, it will shows the detectino markers on the `rviz` and the `image view`. The text in the rviz will also display the current number of lidar detection and apriltag detection pairs. +After the tools detect the LidarTag and AprilTag, it will shows the detection markers on the `rviz` and the `image view`. The text in the rviz will also display the current number of pairs of lidar detections and AprilTag detections. ![segment](../images/tag_based_pnp_calibrator/visualization2.jpg) @@ -81,15 +81,15 @@ After the calibration process is finished, the sensor_calibration_manager will d menu4

-User can modify the `visualization options` in the right side of the `image view`. To compare the results easier, user can set the `Marker size (m)` to `0.04` and set the `PC subsample factor` to `1`. +User can modify the `visualization options` in the right side of the `image view`. To compare the results, please set the `Marker size (m)` to `0.04` and set the `PC subsample factor` to `1`.

visualization_bar

-After setting the options above, change the `/initial_tf` (in the `visualization options`) to `/current_tf`. By doing this, user can easily measure the difference after the calibration. +After setting the options above, change the `/initial_tf` (in the `visualization options`) to `/current_tf`. By doing this, it is easier to measure the difference after the calibration. -The images below show that with the calibrated tranformation, the projected pointcloud align better with the imaage. +The images below show that with the calibrated transformation, the projected point cloud aligns better with the image. diff --git a/sensor/mapping_based_calibrator/README.md b/sensor/mapping_based_calibrator/README.md index 6cde8327..51940473 100644 --- a/sensor/mapping_based_calibrator/README.md +++ b/sensor/mapping_based_calibrator/README.md @@ -15,43 +15,43 @@ Note: depending on how this tool is configured it can perform the following cali ### lidar-lidar calibration -Overview -This algorithm aims to calibrate multiple lidars by using registration algorithms. lidars are seperated in two categories. The main lidar is named `mapping lidar` which aims for build the map. The rest of the lidars are called `calibration lidars`, which are used for calibration, in other words, they are use for finding the transformations between themselves and the mapping lidar. +This algorithm aims to calibrate multiple lidars by using registration algorithms. The lidars are separated into two categories. The main lidar, called the `mapping lidar`, is used for building the map. The rest of the lidars, known as `calibration lidars`, are used for calibration; in other words, they are used to find the transformations between themselves and the mapping lidar. #### Step 1: Mapping (using mapping lidar) -First of all, the calibrator will use one of the lidars (defined in the launch file) as the mapping lidar for mapping. Pointcloud from this lidar uses NDT of GICP algorithm to calculate the pose and also store pointcloud as a map for future usage. +First of all, the calibrator will designate one of the lidars (as defined in the launch file) as the mapping lidar for mapping purposes. The point cloud from this lidar utilizes either the NDT or GICP algorithm to calculate the pose, and also stores the point cloud as a map for future usage. #### Step 2: Calibration data preparation (using calibration lidars) -After the mapping is done, we need to do some preprocessing before the calibration process. For instance, since we want to apply the registration algorithms on the pointcloud from the mapping lidar and the pointcloud from the calibration lidars to find the transformation between two lidar. We need to make sure both of the pointcloud are in the same timestamp. However, the calibration lidars may not be synchronized with the mapping lidar, so their respective pointclouds can not be used directly together under movement. To deal with this, we need to first interpolate the pose of the mapping lidar at the timestamp of the calibration lidars to get the pointcloud for applying registration algorithms. +After the mapping is complete, some preprocessing is necessary before starting the calibration process. For instance, since we aim to apply registration algorithms on the point clouds from both the `mapping lidar` and the `calibration lidars` to find the transformation between the two, it's crucial to ensure that both point clouds share the same timestamp. However, the calibration lidars may not be synchronized with the `mapping lidar`, meaning their respective point clouds cannot be directly used together during movement. To address this, we first need to interpolate the pose of the mapping lidar at the timestamp of the calibration lidars. This step will allow us to correctly align the point clouds for the application of registration algorithms. #### Step 3: Calibrate (mapping lidar & calibration lidars) -After preparing data for the calibration, we can now run the registration algorithms (NDT/GICP) to find the transformations between two pointcloud (calibration lidars' pointcloud and mapping lidar's pointcloud), then we can get the transformation between two lidars. +After preparing the data for calibration, we can now run the registration algorithms (NDT/GICP) to find the transformations between the two point clouds (the calibration lidars' point cloud and the mapping lidar's point cloud). This process allows us to determine the transformation between the two lidars. #### Diagram -Below, you can see the how the algorithm is implemented in the `mapping_based_calibrator` package. +Below, you can see how the algorithm is implemented in the `mapping_based_calibrator` package. + ![segment](../docs/images/mapping_based_calibrator/mapping_based_calibrator.jpg) ### base lidar calibration -Instead of calibrating the transformation between lidar and lidar, we can also utilize the map generated from the mapping lidar to calibration the transformation between mappiong lidar and the base_link. +Instead of calibrating the transformation between lidar and lidar, we can also utilize the map generated by the `mapping lidar` to calibrate the transformation between the `mapping lidar` and the `base_link`. Therefore, step 1 would be the same as `lidar-lidar calibration`. #### Step 1: Mapping (Mapping lidar) -First of all, the calibrator will use one of the lidars (defined in the launch file) as the mapping lidar for mapping. Pointcloud from this lidar uses NDT of GICP algorithm to calculate the pose and also store pointcloud as a map for future usage. +First of all, the calibrator will designate one of the lidars (as defined in the launch file) as the `mapping lidar` for mapping purposes. The point cloud from this lidar utilizes either the NDT or GICP algorithm to calculate the pose and also stores the point cloud as a map for future usage. #### Step 2: Extract ground from the pointcloud -After we build the map, we get dense pointcloud from the map. Then we utilize the PCA and `pcl::SACSegmentation` for the ground extraction. +After building the map, we obtain a dense point cloud from it. We then use PCA and `pcl::SACSegmentation` for ground extraction #### Step 3: Calibrate (using Mapping lidar & baselink) -Finally, we utilize the initial_base_to_lidar_transform and the ground model to calculate the transformation between base_link and mapping lidar. +Finally, we use the initial_base_to_lidar_transform and the ground model to calculate the transformation between the base_link and the `mapping lidar`. ## ROS Interfaces @@ -217,4 +217,4 @@ Note: target_markers is only used by camera calibrators (not using now) ## Pro tips/recommendations -To get better calibration result, move your vehilce as slow as possible. Also, make sure you use the high-resolution Lidar sensor for mapping. +To achieve better calibration results, move your vehicle as slowly as possible. Also, ensure that you use a high-resolution Lidar sensor for mapping. diff --git a/sensor/marker_radar_lidar_calibrator/README.md b/sensor/marker_radar_lidar_calibrator/README.md index bc5d383e..4d0d32ed 100644 --- a/sensor/marker_radar_lidar_calibrator/README.md +++ b/sensor/marker_radar_lidar_calibrator/README.md @@ -12,13 +12,13 @@ The calibration process involves five steps: constructing a background model, de ### Step 1: Background model extraction (radar & lidar) -Once the calibrator starts and the user presses the Extract background model, the calibrator will start to use the lidar pointcloud and radar message to create background models which include voxels that indicate whether it is background or not. +Once the calibrator is initiated and the user presses the `Extract background model` button, the calibrator will begin using the lidar point cloud and radar messages to create background models. These models include voxels that indicate whether they represent the background or not. ### Step 2: Foreground extraction and reflector detection -After the lidar and radar’s background models are created. We can extract the foreground lidar and radar points from the incoming lidar pointcloud and radar message if they are not in the background voxels. +After the background models for the lidar and radar are created, we can extract the foreground lidar and radar points from the incoming lidar point cloud and radar messages if they do not match the background voxels. -All of the foreground radar points are defined as reflector detection. However, foreground lidar points need to apply a clustering algorithm, additional filter, and calculate the cluster's center to become a reflector detection. +All of the foreground radar points are defined as reflector detections. However, to classify foreground lidar points as reflector detections, it is necessary to apply a clustering algorithm, implement additional filtering, and calculate the center of the cluster. ### Step 3: Matching and filtering @@ -26,17 +26,17 @@ Afterward, we match the lidar’s reflector detection and the radar’s reflecti ### Step 4: Calibration -Finally, we can use the coordinate of the reflector detection pairs to calculate the transformation based on e SVD-based estimation. +Finally, we can use the coordinates of the reflector detection pairs to calculate the transformation based on the estimation algorithm selected by the user (e.g., SVD). ### Step 5: Evaluation -Additionally, we provide a metric plotter that can indicate whether the calibration errors are converged. Once the cross-validation errors are converged, users should be able to stop the calibration process. +Additionally, we provide a metric plotter that can indicate whether the calibration errors have converged. Once the cross-validation errors have converged, users should be able to stop the calibration process. ### Diagram Below, you can see the how the algorithm is implemented in the `marker_radar_lidar_calibrator` package. -![segment](../docs/images/marker_radar_lidar_calibrator/marker_radar_lidar_calibrator.jpg) +![marker_radar_lidar_calibrator](../docs/images/marker_radar_lidar_calibrator/marker_radar_lidar_calibrator.jpg) ## ROS Interfaces @@ -123,10 +123,12 @@ Below, you can see the how the algorithm is implemented in the `marker_radar_lid This type of reflector is crucial for such calibrations because it has a highly predictable and consistent response to radar. The triangular shape, often composed of three metal plates arranged in a prism form, ensures that the reflector returns signals in specific, predictable ways. -![segment](../docs/images/marker_radar_lidar_calibrator/radar_reflector.png) +

+ radar_reflector +

## Known issues/limitations ## Pro tips/recommendations -While doing the calibration, try to set different height for the radar reflecotr by using the tripod and also place the reflector in differnt distance. Please also make sure the center of the radar reflecotr face to the radar sensor. +While performing the calibration, try setting the radar reflector at different heights using the tripod, and also place the reflector at various distances. Please also ensure that the center of the radar reflector faces the radar sensor. diff --git a/sensor/tag_based_pnp_calibrator/README.md b/sensor/tag_based_pnp_calibrator/README.md index 2fb7c024..57303b6f 100644 --- a/sensor/tag_based_pnp_calibrator/README.md +++ b/sensor/tag_based_pnp_calibrator/README.md @@ -8,11 +8,11 @@ The package `tag_based_pnp_calibrator` allows extrinsic calibration among Camera ## Inner-workings / Algorithms -The `tag_based_pnp_calibrator` utilize the PNP algorithm to calculate the transformation between lidar and Camera. To run this package, you also need to run `apriltag_ros` package and `lidartag` package to calculate the transformation. +The `tag_based_pnp_calibrator` utilizes the PnP algorithm to calculate the transformation between the lidar and camera. To run this package, you also need to operate the `apriltag_ros` package and the `lidartag` package to calculate the transformation. -The `apriltag_ros` package will detect the apriltag and output the apriltag detection. On the other hand, `lidartag` package will detect the lidartag and output the lidartag detection. +The `apriltag_ros` package detects the AprilTag and outputs the detection results. Conversely, the `lidartag` package detects the LidarTag and outputs its detection results. -The `tag_based_pnp_calibrator` utilize the detections from `apriltag_ros` and `lidartag`, and use Kalman Filter to track those detections. If the detections is converged, then the calibrator will apply SQPNP provided by OpenCV to estimate the transformation between image points from apriltag and object points from lidartag. +The `tag_based_pnp_calibrator` utilizes the detections from both apriltag_ros and lidartag, employing a Kalman Filter to track these detections. If the detections converge, the calibrator applies the SQPnP algorithm provided by OpenCV to estimate the transformation between the image points from AprilTag and the object points from LidarTag. ### Diagram @@ -27,8 +27,8 @@ Below, you can see the how the algorithm is implemented in the `tag_based_pnp_ca | Name | Type | Description | | --------------------------- | -------------------------------------------- | ------------------------------------------------------------------------------------------- | | `{camera_info}` | `sensor_msgs::msg::CameraInfo` | Intrinsic parameters for the calibration cameras . `camera_info` is provided via parameters | -| `lidartag/detections_array` | `lidartag_msgs::msg::LidarTagDetectionArray` | Lidartag detections. `lidartag/detections_array` is defined in launcher. | -| `apriltag/detection_array` | `apriltag_msgs::msg::AprilTagDetectionArray` | Apriltag detections. `apriltag/detection_array` is defined in launcher. | +| `lidartag/detections_array` | `lidartag_msgs::msg::LidarTagDetectionArray` | LidarTag detections. `lidartag/detections_array` is defined in launcher. | +| `apriltag/detection_array` | `apriltag_msgs::msg::AprilTagDetectionArray` | AprilTag detections. `apriltag/detection_array` is defined in launcher. | ### Output @@ -51,13 +51,13 @@ Below, you can see the how the algorithm is implemented in the `tag_based_pnp_ca | --------------------------------------------- | --------------------- | ------------- | ----------------------------------------------------------------------------------------------------------------------------------- | | `calib_rate` | `double` | | The rate at which the calibration callback is invoked. This controls the frequency of calibration updates. | | `base_frame` | `std::string` | | The base_frame is used to compare the initial and calibrated values. | -| `min_tag_size` | `double` | | The size of the Apriltag in meters | +| `min_tag_size` | `double` | | The size of the AprilTag in meters | | `max_tag_distance` | `double` | | Maximum allowable distance from the camera to the tags. | -| `max_allowed_homography_error` | `double` | | Discard the Apriltag detection if the homography error is larger than this value. | +| `max_allowed_homography_error` | `double` | | Discard the AprilTag detection if the homography error is larger than this value. | | `use_receive_time` | `bool` | | Flag to determine whether to use the receive time instead of the header timestamps. | | `use_rectified_image` | `bool` | | Flag to determine whether rectified images should be used in the calibration process. | | `calibration_crossvalidation_training_ratio` | `double` | | The ratio of data used for training versus testing during the calibration's cross-validation process. | -| `calibration_convergence_min_pairs` | `int` | | The minimum number of Apriltag and LiDARtag detection pairs required to consider the calibration process as potentially converging. | +| `calibration_convergence_min_pairs` | `int` | | The minimum number of AprilTag and LidarTag detection pairs required to consider the calibration process as potentially converging. | | `calibration_convergence_min_area_percentage` | `double` | | Minimum percentage of the area that needs to be covered by detection. | | `min_pnp_points` | `int` | | Minimum number of points required for the Perspective-n-Point problem used in calibration to solve the pose estimation. | | `min_convergence_time` | `double` | | Minimum time required for the calibration process to be considered as converged. | @@ -86,9 +86,11 @@ Below, you can see the how the algorithm is implemented in the `tag_based_pnp_ca ### LiDARTag -In order the perform camera-lidar calibration using this tool, it is necessary to prepare lidartags and lidars with intensity measures. In order to assure that no objects difficult the tag detection and and obtain the most stable detection possible, it is highly recommended to also prepare fixed mounts for these tags as presented in below. +To perform camera-lidar calibration using this tool, it is necessary to prepare LidarTags and lidars with intensity measures. To ensure that no objects obstruct the tag detection and to achieve the most stable detection possible, it is highly recommended to also prepare fixed mounts for these tags, as shown below. -![segment](../docs/images/tag_based_pnp_calibrator/lidartag-mount.jpg) +

+ lidartag-mount +

## References @@ -97,16 +99,20 @@ References/External links ## Known issues/limitations -Our version of lidartag only supports the family `16h5` +Our version of LidarTag only supports the family `16h5` -Our codebase only supports apriltag detections for `36h11` +Our codebase only supports AprilTag detections for `36h11` ## Pro tips/recommendations -During calibration, ensure that the lidar scan cover the tag, similar to the first example shown in below image. However, if the tag resolution is low, as in the second example, and the lidar still detects the tag, it is acceptable. The third example demonstrates a scenario where the lidar scan fails to cover the tag, resulting in the inability to detect the lidartag. +During calibration, ensure that the lidar scan covers the tag, similar to the first example shown in the image below. However, if the tag resolution is low, as in the second example, and the lidar still detects the tag, it is acceptable. The third example demonstrates a scenario where the lidar scan fails to cover the tag, resulting in the inability to detect the LidarTag. -![segment](../docs/images/tag_based_pnp_calibrator/lidarscan_on_tag.jpg) +

+ lidarscan_on_tag +

Also noted that when doing the calibration, it is necessary to rotate the tag in order to face to the camera like the image shown below. -![segment](../docs/images/tag_based_pnp_calibrator/tag_position.jpg) +

+ tag_position +

From 374be879f32dfd410cdc3cbf540ea326e5ee4fe1 Mon Sep 17 00:00:00 2001 From: vividf Date: Wed, 24 Apr 2024 19:42:44 +0900 Subject: [PATCH 6/7] add console text Signed-off-by: vividf --- sensor/docs/tutorials/marker_radar_lidar_calibrator.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/sensor/docs/tutorials/marker_radar_lidar_calibrator.md b/sensor/docs/tutorials/marker_radar_lidar_calibrator.md index 569793c8..782f957a 100644 --- a/sensor/docs/tutorials/marker_radar_lidar_calibrator.md +++ b/sensor/docs/tutorials/marker_radar_lidar_calibrator.md @@ -107,6 +107,12 @@ Using the tutorial rosbag as an example, we can delete the latest radar and lida
+The console should also show the following text. + +```bash +[marker_radar_lidar_calibrator]: The last track was successfully deleted. Remaining converged tracks: 4 +``` + ### Metric plotter The tool also provides a metric plotter for real time visualization shown in below. From efe2ca21bca00645314bcfdeec7250ddda345820 Mon Sep 17 00:00:00 2001 From: vividf Date: Wed, 24 Apr 2024 19:47:27 +0900 Subject: [PATCH 7/7] fix image Signed-off-by: vividf --- sensor/docs/tutorials/marker_radar_lidar_calibrator.md | 4 +++- sensor/docs/tutorials/tag_based_pnp_calibrator.md | 2 +- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/sensor/docs/tutorials/marker_radar_lidar_calibrator.md b/sensor/docs/tutorials/marker_radar_lidar_calibrator.md index 782f957a..b3f0308a 100644 --- a/sensor/docs/tutorials/marker_radar_lidar_calibrator.md +++ b/sensor/docs/tutorials/marker_radar_lidar_calibrator.md @@ -36,7 +36,9 @@ ros2 run sensor_calibration_manager sensor_calibration_manager In `project`, select `x2`, and in `calibrator`, select `marker_radar_lidar_calibrator`. Then, press `Continue`. -![marker_radar_lidar_calibrator](../images/marker_radar_lidar_calibrator/menu1.jpg) +

+ menu1 +

A menu titled `Launcher configuration` should appear in the UI, and the user may change any parameter he deems convenient. For this tutorial, we will modify the default value `radar_name` from `front_left` to `front_center`. For the `msg_type` and `transformation_type`, as `object_raw` topic is type of `radar_tracks` and the radar type is 2D in this tutorial, we keep them as default. After configuring the parameters, click `Launch`. diff --git a/sensor/docs/tutorials/tag_based_pnp_calibrator.md b/sensor/docs/tutorials/tag_based_pnp_calibrator.md index e2c9fecd..7ad1bcb5 100644 --- a/sensor/docs/tutorials/tag_based_pnp_calibrator.md +++ b/sensor/docs/tutorials/tag_based_pnp_calibrator.md @@ -40,7 +40,7 @@ ros2 run sensor_calibration_manager sensor_calibration_manager In `project`, select `x2`, and in `calibrator`, select `tag_based_pnp_calibrator`. Then, press `Continue`.

- menu1 + menu1

A menu titled `Launcher configuration` should appear in the UI, and the user may change any parameter he deems convenient.

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