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DCL-SLAM

A ROS package of DCL-SLAM: Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm.

10.11._2k_2023118104959.mp4

The HD video of the demonstration of DCL-SLAM is avaliable at BiliBili.

Prerequisites

  • Ubuntu ROS (Robot Operating System on Ubuntu 18.04 or 20.04)
  • Python (For wstool and catkin tool)
  • CMake (Compilation Configuration Tool)
  • Boost (portable C++ source libraries)
  • PCL (Default Point Cloud Library on Ubuntu work normally)
  • Eigen (Default Eigen library on Ubuntu work normally)
sudo apt-get install cmake libboost-all-dev python-wstool python-catkin-tools

These prerequisites will be installed during the compilation.

Compilation

Set up the workspace configuration:

mkdir -p ~/cslam_ws/src
cd ~/cslam_ws
catkin init
catkin config --merge-devel
catkin config --cmake-args -DCMAKE_BUILD_TYPE=Release

Then use wstool for fetching catkin dependencies:

cd src
git clone https://github.com/PengYu-Team/DCL-SLAM.git
git clone https://github.com/PengYu-Team/DCL-LIO-SAM.git
git clone https://github.com/PengYu-Team/DCL-FAST-LIO.git
wstool init
wstool merge DCL-SLAM/dependencies.rosinstall
wstool update

Build DCL-SLAM

catkin build dcl_lio_sam
catkin build dcl_fast_lio

Run with Dataset

  • S3E dataset. The datasets are configured to run with default parameter.
roslaunch dcl_slam run.launch
rosbag play *your-bag-path*.bag
  • Other dataset. Please follow LIO-SAM and FAST-LIO2 to set your own config file for the dataset in "config/your-config-file.yaml", and change the path in "launch/single_ugv.launch".

Citation

This work is published in IEEE Sensors Journal 2023, and please cite related papers:

@ARTICLE{10375928,
  author={Zhong, Shipeng and Qi, Yuhua and Chen, Zhiqiang and Wu, Jin and Chen, Hongbo and Liu, Ming},
  journal={IEEE Sensors Journal}, 
  title={DCL-SLAM: A Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm}, 
  year={2024},
  volume={24},
  number={4},
  pages={4786-4797},
  keywords={Simultaneous localization and mapping;Laser radar;Sensors;Optimization;Feature extraction;Trajectory;Odometry;Collaborative localization;distributed framework;place recognition;range sensor},
  doi={10.1109/JSEN.2023.3345541}}

@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}
}

Acknowledgement

  • DCL-LIO-SAM adopt LIO-SAM as front end (Shan, Tixiao and Englot, Brendan and Meyers, Drew and Wang, Wei and Ratti, Carlo and Rus Daniela. LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping).

  • DCL-FAST-LIO adopt FAST-LIO2 as front end (Xu, Wei, Yixi Cai, Dongjiao He, Jiarong Lin, and Fu Zhang. Fast-lio2: Fast direct lidar-inertial odometry).

  • DCL-SLAM is based on a two-stage distributed Gauss-Seidel approach (Siddharth Choudhary and Luca Carlone and Carlos Nieto and John Rogers and Henrik I. Christensen and Frank Dellaert. Distributed Trajectory Estimation with Privacy and Communication Constraints: a Two-Stage Distributed Gauss-Seidel Approach).

  • DCL-SLAM is based on outlier rejection of DOOR-SLAM (Lajoie, Pierre-Yves and Ramtoula, Benjamin and Chang, Yun and Carlone, Luca and Beltrame, Giovanni. DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams).