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Repository Summary
| Description | A ROS2 package of CoLRIO: LiDAR-Ranging-Inertial Centralized State Estimation for Robotic Swarms. |
| Checkout URI | https://github.com/zhongshp/co-lrio.git |
| VCS Type | git |
| VCS Version | master |
| Last Updated | 2025-03-27 |
| Dev Status | UNKNOWN |
| CI status | No Continuous Integration |
| Released | UNRELEASED |
| Tags | centralized collaborative-slam lidar-ranging-inertial |
| Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Packages
| Name | Version |
|---|---|
| co_lrio | 1.0.0 |
README
CoLRIO
A ROS2 package of CoLRIO: LiDAR-Ranging-Inertial Centralized State Estimation for Robotic Swarms.
https://github.com/PengYu-Team/zhongshp/assets/41199568/81985d82-983c-4eca-898b-43e8f84e7b45
Author
Shipeng Zhong & Dapeng Feng & Zhiqiang Chen
Prerequisites
- Ubuntu ROS2 Foxy (Robot Operating System 2 on Ubuntu 20.04)
- CMake (Compilation Configuration Tool)
- PCL (Default Point Cloud Library on Ubuntu work normally)
- Eigen (Default Eigen library on Ubuntu work normally)
- GTSAM 4.2a8 (Georgia Tech Smoothing and Mapping library)
Compilation
Build CoLRIO:
mkdir -p ~/cslam_ws/src
cd ~/cslam_ws/src
git clone https://github.com/zhongshp/Co-LRIO.git
cd ../
colcon build --symlink-install
Run with Dataset
- S3E dataset. The datasets are configured to run with default parameter.
ros2 launch co_lrio run.launch.py
ros2 bag play *your-bag-path*
- [our dataset] please also found it in S3E dataset.
Citation
This work is published in IEEE ICRA 2024 conference, and please cite related papers:
@INPROCEEDINGS{10611672,
author={Zhong, Shipeng and Chen, Hongbo and Qi, Yuhua and Feng, Dapeng and Chen, Zhiqiang and Wu, Jin and Wen, Weisong and Liu, Ming},
booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
title={CoLRIO: LiDAR-Ranging-Inertial Centralized State Estimation for Robotic Swarms},
year={2024},
volume={},
number={},
pages={3920-3926},
keywords={Simultaneous localization and mapping;Accuracy;Scalability;Collaboration;Computational efficiency;Sensors;Servers},
doi={10.1109/ICRA57147.2024.10611672}}
@ARTICLE{10740801,
author={Feng, Dapeng and Qi, Yuhua and Zhong, Shipeng and Chen, Zhiqiang and Chen, Qiming and Chen, Hongbo and Wu, Jin and Ma, Jun},
journal={IEEE Robotics and Automation Letters},
title={S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM},
year={2024},
volume={9},
number={12},
pages={11401-11408},
keywords={Simultaneous localization and mapping;Robot sensing systems;Synchronization;Trajectory;Global navigation satellite system;Collaboration;Accuracy;Motion capture;Robot localization;Multi-robot systems;Multi-robot SLAM;data sets for SLAM;SLAM},
doi={10.1109/LRA.2024.3490402}}
Acknowledgement
- We combined the front end of CoLRIO and the DLO to achieve the 5th position in the ICCV 2023 LiDAR-Inertial SLAM Challenge.
The Leaderboard is shown as follow:
And the hardware and results are shown as follow:
-
CoLRIO depends on FAST-GICP (Kenji Koide, Masashi Yokozuka, Shuji Oishi, and Atsuhiko Banno, “Voxelized GICP for fast and accurate 3D point cloud registration”.).
-
CoLRIO depends on GncOptimizer (Yang, Antonante, Tzoumas, Carlone, “Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection”).
CONTRIBUTING
No CONTRIBUTING.md found.
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