AFLI-Calib: Robust LiDAR-IMU extrinsic self-calibration based on adaptive frame length LiDAR odometry

This is the official implementation (refactor version based on the experiment code) of the following publication:

AFLI-Calib: Robust LiDAR-IMU extrinsic self-calibration based on adaptive frame length LiDAR odometry
Weitong Wu, Jianping Li, Chi Chen, Bisheng Yang, Xianghong Zou, Yandi Yang, Yuhang Xu, Ruofei Zhong, Ruibo Chen
ISPRS Journal of Photogrammetry and Remote Sensing,2023,199:157-181
Paper

🔭 Introduction

Abstract: As an effective complement to common laser scanning systems, the portable laser scanning system can acquire point clouds flexibly and quickly. Calibration between Light detection and ranging (LiDAR) sensors and inertial measurement units (IMU) is the prerequisite for laser scanning systems to obtain high-quality point clouds. Related methods have been proposed in the last two decades, where the global navigation satellite system (GNSS) or high-precision calibration fields are commonly used. However, the extrinsic self-calibration of LiDAR-IMU is challenging, due to the large distortion in single-frame point cloud caused by rapid motion and the position errors of IMU integration which drift quickly. At the same time, the highly dynamic motion patterns of portable devices and the changes in the scanned scene structure are not well considered in existing LiDAR odometry methods. To take better advantage of the characteristics of non-repetitive scanning LiDAR sensor, this paper proposes AFLI-Calib, which utilizes adaptive frame length LiDAR odometry to perform the extrinsic self-calibration of LiDAR-IMU. Unlike LiDAR odometry methods with a fixed frame length, the LiDAR frame length is dynamically adjusted according to the motion state of sensors and the matching stability of scenes. The single-frame point cloud is registered to the map through a linear-based continuous-time model, eliminating the motion distortion correction in advance. For further optimization of trajectory and extrinsic parameters, IMU raw measurements and LiDAR observations are involved in the multi-constraint optimization, through tightly-coupled IMU pre-integration constraints, LiDAR point-to-plane constraints, and prior constraints. The method is fully validated using self-collected calibration data of indoor and outdoor scenes and different motion modes. Experiments show that on the test data, the translation parameter accuracy of the method is 0.041 m, which is 56.3% higher than the state-of-the-art method. The standard deviation is significantly reduced, with translation deviation (0.017 m, 0.024 m, 0.022 m) and rotation deviation (0.17◦, 0.25◦, 0.15◦), which verifies the robustness of our method. The average RMSE of distances to the reference point cloud acquired by the terrestrial laser scanning system (TLS) is 0.042 m, showing a high accuracy calibration result. Comparative experiments with the fixed frame length LiDAR odometry method and classical “correction-then-registration” motion distortion model further verify the superiority and effectiveness of the proposed adaptive frame length LiDAR odometry.

🔗 Related Works

Dataset:

WHU-Helmet Dataset: A helmet-based multi-sensor SLAM dataset for the evaluation of real-time 3D mapping in large-scale GNSS-denied environments

💻 Requirements

The code has been tested on:

• Ubuntu 18.04
• ROS melodic
• GTSAM 4.0.3
• Ceres 2.1.0

✏️ Build & Run

1. How to build this project

cd ~/catkin_ws/src
git clone https://github.com/DCSI2022/AFLI_Calib.git
cd AFLI_Calib
catkin_make

Need solve the dependency before catkin_make, or use Docker

Docker (Recommended)

docker build -t $image_name:tag . #build custom name and tag from Dockerfile
docker run -it -v ~/catkin_ws/src/AFLI_Calib:/home/catkin_ws/src/AFLI_Calib --network host $image_name:tag
cd /home/catkin_ws # in container
catkin_make # in container 

RUN AFLO

rosrun afli_calib afl_lidarOdometry $rosbag_path $lidar_type $lidar_topic $match_stability_threshold $motion_linearity_threshold $rosbag_start $rosbag_end

lidar_type: 1 LIVOX 2 VELODYNE 3 OUSTER 4 HESAI

RUN LiDAR-IMU extrinsic calibration

rosrun afli_calib tight_licalib $rosbag_path $lo_path $lidar_type $lidar_topic $rosbag_start $rosbag_end %still_time

Todo

• Modify parameters using yaml file

💡 Citation

If you find this repo helpful, please give us a star . Please consider citing AFLI-Calib if this program benefits your project

@article{wu2023afli,
  title={AFLI-Calib: Robust LiDAR-IMU extrinsic self-calibration based on adaptive frame length LiDAR odometry},
  author={Wu, Weitong and Li, Jianping and Chen, Chi and Yang, Bisheng and Zou, Xianghong and Yang, Yandi and Xu, Yuhang and Zhong, Ruofei and Chen, Ruibo},
  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
  volume={199},
  pages={157--181},
  year={2023},
  publisher={Elsevier}
}

🔗 Acknowledgments

We sincerely thank the excellent projects:

• loam_livox for inspiring the idea of the adaptive frame length
• ikd-Tree for point cloud map management;
• GTSAM for IMU pre-integration and factor graph optimization;
• Ceres for auto-diff.
• Sopuhs
• PCL
• A-LOAM