Better and more organized MLCC

Some soft engineering to make original mlcc easier to use and to be compared with. Original branch of it see main.

1. Prerequisites

Our code has been tested on Ubuntu 16.04 with ROS Kinetic, Ubuntu 18.04 with ROS Melodic and Ubuntu 20.04 with ROS Noetic, Ceres Solver 1.14.x, OpenCV 3.4.14, Eigen 3.3.7, PCL 1.8.

2. Build and Run

Clone the repository and catkin build it.

3. Run Our Example

Be carefule, the format of pose is xyz-wxyz.

Just a heads up, I clear the git cache of dataset folder for faster push and pull. You can fetch the dataset from the original branch main.

The parameters base LiDAR (AVIA or MID), test scene (scene-1 or scene-2), adaptive_voxel_size, etc., could be modified in the corresponding launch file. We also provide the original rosbag files (scene-1 and scene-2) for your reference.

3.1 Multi-LiDAR Extrinsic Calibration

Step 1: base LiDAR pose optimization (the initial pose is stored in scene-x/original_pose)

roslaunch mlcc pose_refine.launch

Step 2: LiDAR extrinsic optimization (the initial extrinsic is stored in config/init_extrinsic)

roslaunch mlcc extrinsic_refine.launch

Step 3: pose and extrinsic joint optimization

roslaunch mlcc global_refine.launch

3.2 Multi-LiADR-Camera Extrinsic Calibration

roslaunch mlcc calib_camera.launch

4. Run Your Own Data

To test on your own data, you need to save the LiDAR point cloud in .pcd format. Please only collect the point cloud and images when the LiDAR (sensor platform) is not moving for optimal precision (or segment them from a complete rosbag). The base LiDAR poses and initial extrinsic values shall also be provided (in tx ty tz qw qx qy qz format). These initial values could be obtained by general SLAM and hand-eye calibration algorithms.

You may need to modify the parameters voxel_size (adaptive voxel size), feat_eigen_limit (feature eigen ratio), and downsmp_sz_base (downsampling size) for LiDAR-LiDAR extrinsic calibration to adjust the precision and speed. You need to change the corresponding path and topic name in the yaml files in the config folder.

5. License

The source code is released under GPLv2 license.

We are still working on improving the performance and reliability of our codes. For any technical issues, please contact us via email xliuaa@connect.hku.hk and xy19980205@outlook.com.

For commercial use, please contact Dr. Fu Zhang fuzhang@hku.hk.

@ARTICLE{9779777,
  author={Liu, Xiyuan and Yuan, Chongjian and Zhang, Fu},
  journal={IEEE Transactions on Instrumentation and Measurement},
  title={Targetless Extrinsic Calibration of Multiple Small FoV LiDARs and Cameras Using Adaptive Voxelization},
  year={2022},
  volume={71},
  number={},
  pages={1-12},
  doi={10.1109/TIM.2022.3176889}
}

Rick0514 / mlcc