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LIO_SAM#

What is LIO_SAM?#

  • A framework that achieves highly accurate, real-time mobile robot trajectory estimation and map-building. It formulates lidar-inertial odometry atop a factor graph, allowing a multitude of relative and absolute measurements, including loop closures, to be incorporated from different sources as factors into the system

Repository Information#

https://github.com/TixiaoShan/LIO-SAM

Required Sensors#

  • LIDAR [Livox, Velodyne, Ouster]
  • IMU [9-AXIS]
  • GPS [OPTIONAL]

drawing

ROS Compatibility#

Dependencies#

  • ROS
  • PCL

  • Gtsam (Georgia Tech Smoothing and Mapping library)

    sudo add-apt-repository ppa:borglab/gtsam-release-4.0
sudo apt install libgtsam-dev libgtsam-unstable-dev

  sudo apt-get install -y ros-melodic-navigation
  sudo apt-get install -y ros-melodic-robot-localization
  sudo apt-get install -y ros-melodic-robot-state-publisher

Build & Run#

1) Build#

    mkdir -p ~/catkin_lio_sam/src
    cd ~/catkin_lio_sam/src
    git clone https://github.com/TixiaoShan/LIO-SAM.git
    cd ..
    catkin_make
    source devel/setup.bash

2) Set parameters#

  • Set topics and sensor settings on lio_sam/config/params.yaml

3) Run#

    # Run the Launch File
      roslaunch lio_sam run.launch

    # Play bag file in the other terminal
      rosbag play xxx.bag --clock

Example Result#

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Paper#

Thank you for citing LIO-SAM (IROS-2020) if you use any of this code.

@inproceedings{liosam2020shan,
  title={LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping},
  author={Shan, Tixiao and Englot, Brendan and Meyers, Drew and Wang, Wei and Ratti, Carlo and Rus Daniela},
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={5135-5142},
  year={2020},
  organization={IEEE}
}

Part of the code is adapted from LeGO-LOAM.

@inproceedings{legoloam2018shan,
  title={LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain},
  author={Shan, Tixiao and Englot, Brendan},
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={4758-4765},
  year={2018},
  organization={IEEE}
}

Acknowledgements#

  • LIO-SAM is based on LOAM (J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time).