LiDAR_Localization_100FPS

LiDAR_Localization_100FPS, referred to as LiDAR-ReLoc, is a straightforward yet effective relocalization solution that leverages a point cloud map and a single LiDAR scan. It is specifically designed to efficiently (100 FPS) restore the global 3-DOF pose of a robot that has been kidnapped or lost.

This method adopts an offline-online approach, where maps are created and the database is collected offline once, enabling multiple instances of online relocalization.

Note: The current version eliminates KD trees, which slightly impact relocalization efficiency.

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Note: The left side shows the localization illustration, while the right displays a simple descriptor.

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Citation

• LiDAR-ReLoc has been accepted for JAG: Published Paper.
• If you use code or data of LiDAR-ReLoc in your academic research, please cite our paper:

@article{shi2023lidar,
  title={LiDAR localization at 100 FPS: a map-aided and template descriptor-based global method},
  author={Shi, Pengcheng and Li, Jiayuan and Zhang, Yongjun},
  journal={International Journal of Applied Earth Observation and Geoinformation},
  volume={120},
  pages={103336},
  year={2023},
  publisher={Elsevier}
}

Test Environments

- Ubuntu 20.04
- ROS noetic
- PCL
- Eigen
- OpenMP

File Structure

Using KITTI as an example, despite issues with the correction file, it is utilized to convert the camera pose to the LiDAR-centered coordinate system for comparison consistency.

1. KITTI dataset

├── kitti
│   └── 07
│       ├── lidar_pose.txt
│       ├── times.txt
│       └── velodyne
│             ├──  000000.bin
│             ├──  000001.bin
│             └──  ...

Note: In lidar_pose.txt, each row represents a LiDAR-centered pose in KITTI format, i.e., a 3x4 matrix.

2. Map directory

A "database" subdirectory should be created within the "map" directory, and all files generated during offline map processing will be stored within these directories.

├── kitti
│   └── 07
│       └── map
│           ├── candidate_pts.pcd 
│           ├── pass_map.pcd
│           ├── raw_map.pcd
│           └── database
│                  ├── 0.bin
│                  ├── 1.bin
│                  ├── ...
│                  └── 1600.bin

Run the package

1. download and build the code

$ git clone https://github.com/ShiPC-AI/LiDAR-Localization-100FPS.git
// move the folder to your catkin/src/
// cd the folder of catkin
$ catkin_make

Note: Prepare the KITTI or other datasets beforehand.

2. offline mapping

$ roslaunch lidar_reloc mapping.launch
// a new terminal 
$ roslaunch lidar_reloc play_kitti_for_mapping.launch

Note: The purple represents virtual points generated from the ground, while the others denote map points.

3. offline collecteing database:

// a new terminal
$ roslaunch lidar_reloc collect_database.launch

Note: The left side shows the terminal output of the "collect database", while the right displays a portion of the map database.

4. online relocalization

$ roslaunch lidar_reloc relocate.launch
// Wait for RViz to load the map (about 1 second)
// a new terminal
$ roslaunch lidar_reloc play_kitti_for_relocate.launch

Other Examples

1. KITTI 06

2. Parking Lot

3. Corridor

Contact

For any inquiries, feel free to contact me:

• Pengcheng Shi {shipc_2021@whu.edu.cn}

Acknowledgments

Thanks for linefit.

Todo List

• Add publised paper
• Add demo examples
• Add ROS support
• Eliminate th reliance on the offline mapping process.