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https://github.com/PengYu-Team/DCL-SLAM

A ROS package of DCL-SLAM: Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm.
https://github.com/PengYu-Team/DCL-SLAM

collaborative-slam distributed-systems lidar

Last synced: 2 months ago
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A ROS package of DCL-SLAM: Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm.

Host: GitHub
URL: https://github.com/PengYu-Team/DCL-SLAM
Owner: PengYu-Team
License: apache-2.0
Created: 2022-10-20T08:55:36.000Z (over 1 year ago)
Default Branch: master
Last Pushed: 2024-02-29T07:45:18.000Z (4 months ago)
Last Synced: 2024-02-29T08:43:38.744Z (4 months ago)
Topics: collaborative-slam, distributed-systems, lidar
Language: C++
Homepage:
Size: 72.3 KB
Stars: 155
Watchers: 8
Forks: 15
Open Issues: 17
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

awesome-stars - PengYu-Team/DCL-SLAM - A ROS package of DCL-SLAM: Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm. (C++)
awesome-mobile-robotics - DCL-SLAM

README

        # DCL-SLAM

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

https://user-images.githubusercontent.com/41199568/213071890-679025cf-23f5-48f0-a2ef-d9b00911f926.mp4

The HD video of the demonstration of DCL-SLAM is avaliable at [BiliBili](https://www.bilibili.com/video/BV12G4y187mw/?spm_id_from=333.337.search-card.all.click).

## Prerequisites

  - [Ubuntu ROS](http://wiki.ros.org/ROS/Installation) (Robot Operating System on Ubuntu 18.04 or 20.04)

  - Python (For wstool and catkin tool)

  - CMake (Compilation Configuration Tool)

  - [Boost](http://www.boost.org/) (portable C++ source libraries)

  - [PCL](https://pointclouds.org/downloads/linux.html) (Default Point Cloud Library on Ubuntu work normally)

  - [Eigen](http://eigen.tuxfamily.org/index.php?title=Main_Page) (Default Eigen library on Ubuntu work normally)

  ```

  sudo apt-get install cmake libboost-all-dev python-wstool python-catkin-tools

  ```

  - [GTSAM](https://github.com/borglab/gtsam/releases) (Georgia Tech Smoothing and Mapping library)

  - [livox_ros_driver](https://github.com/Livox-SDK/livox_ros_driver) (The driver for Livox LiDAR)

  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](https://github.com/PengYu-Team/S3E). 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](https://github.com/TixiaoShan/LIO-SAM) and [FAST-LIO2](https://github.com/hku-mars/FAST_LIO) 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](https://github.com/TixiaoShan/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](https://github.com/hku-mars/FAST_LIO) 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](https://github.com/lajoiepy/robust_distributed_mapper) (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).