A ROS2 package of CoLRIO: LiDAR-Ranging-Inertial Centralized State Estimation for Robotic Swarms.
https://github.com/PengYu-Team/Co-LRIO/assets/41199568/81985d82-983c-4eca-898b-43e8f84e7b45
Build CoLRIO:
mkdir -p ~/cslam_ws/src
cd ~/cslam_ws/src
git clone https://github.com/PengYu-Team/Co-LRIO.git
cd ../
colcon build --symlink-install
[our dataset] TBD.
S3E dataset. The datasets are configured to run with default parameter.
ros2 launch co_lrio run.launch.py
ros2 bag play *your-bag-path*
This work is published in IEEE ICRA 2024 conference, and please cite related papers:
@misc{zhong2024colrio,
title={CoLRIO: LiDAR-Ranging-Inertial Centralized State Estimation for Robotic Swarms},
author={Shipeng Zhong and Hongbo Chen and Yuhua Qi and Dapeng Feng and Zhiqiang Chen and Jin Wu and Weisong Wen and Ming Liu},
year={2024},
eprint={2402.11790},
archivePrefix={arXiv},
primaryClass={cs.RO}
}
@article{feng2022s3e,
title={S3e: A large-scale multimodal dataset for collaborative slam},
author={Feng, Dapeng and Qi, Yuhua and Zhong, Shipeng and Chen, Zhiqiang and Jiao, Yudu and Chen, Qiming and Jiang, Tao and Chen, Hongbo},
journal={arXiv preprint arXiv:2210.13723},
year={2022}
}
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").