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IA-LIO-SAM#

What is IA-LIO-SAM?#

  • IA_LIO_SLAM is created for data acquisition in unstructured environment and it is a framework for Intensity and Ambient Enhanced Lidar Inertial Odometry via Smoothing and Mapping that achieves highly accurate robot trajectories and mapping.

Repository Information#

https://github.com/minwoo0611/IA_LIO_SAM

Required Sensors#

  • LIDAR [Velodyne, Ouster]
  • IMU [9-AXIS]
  • GNSS

ROS Compatibility#

  • ROS 1

Dependencies#

  • ROS (tested with Kinetic and Melodic)

    • for ROS melodic:

      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

    • for ROS kinetic:

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

  • GTSAM (Georgia Tech Smoothing and Mapping library) 

    wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.2.zip
cd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/
cd ~/Downloads/gtsam-4.0.2/
mkdir build && cd build
cmake -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF ..
sudo make install -j8

Build & Run#

1) Build#

    mkdir -p ~/catkin_ia_lio/src
    cd ~/catkin_ia_lio/src
    git clone https://github.com/minwoo0611/IA_LIO_SAM
    cd ..
    catkin_make

2) Set parameters#

  • After downloading the repository, change topic and sensor settings on the config file (workspace/src/IA_LIO_SAM/config/params.yaml)
  • For imu-lidar compatibility, extrinsic matrices from calibration must be changed.

Extrinsic Matrices

  • To enable autosave, savePCD must be true on the params.yaml file (workspace/src/IA_LIO_SAM/config/params.yaml).

3) Run#

  # open new terminal: run IA_LIO
  source devel/setup.bash
  roslaunch lio_sam mapping_ouster64.launch

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

Sample dataset images#

drawing drawing drawing

Example dataset#

Check original repo link for example dataset.

Contact#

  • Maintainer: Kevin Jung (GitHub: minwoo0611)

Paper#

Thank you for citing IA-LIO-SAM(./config/doc/KRS-2021-17.pdf) if you use any of this code.

Part of the code is adapted from LIO-SAM (IROS-2020).

@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#

  • IA-LIO-SAM is based on LIO-SAM (T. Shan, B. Englot, D. Meyers, W. Wang, C. Ratti, and D. Rus. LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping).