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LVI-SAM

This repository contains code for a lidar-visual-inertial odometry and mapping system, which combines the advantages of LIO-SAM and Vins-Mono at a system level.

drawing

Dependency

  • ROS (Tested with kinetic and melodic)
  • 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
  • Ceres (C++ library for modeling and solving large, complicated optimization problems) 
    sudo apt-get install -y libgoogle-glog-dev
sudo apt-get install -y libatlas-base-dev
wget -O ~/Downloads/ceres.zip https://github.com/ceres-solver/ceres-solver/archive/1.14.0.zip
cd ~/Downloads/ && unzip ceres.zip -d ~/Downloads/
cd ~/Downloads/ceres-solver-1.14.0
mkdir ceres-bin && cd ceres-bin
cmake ..
sudo make install -j4

Getting start with Docker

When you use Docker, you could solve the dependency at once.
For more information, you can check docker_start.md.

Compile

You can use the following commands to download and compile the package.

cd ~/catkin_ws/src
git clone https://github.com/TixiaoShan/LVI-SAM.git
cd ..
catkin_make

Datasets

drawing

The datasets used in the paper can be downloaded from Google Drive. The data-gathering sensor suite includes: Velodyne VLP-16 lidar, FLIR BFS-U3-04S2M-CS camera, MicroStrain 3DM-GX5-25 IMU, and Reach RS+ GPS.

https://drive.google.com/drive/folders/1q2NZnsgNmezFemoxhHnrDnp1JV_bqrgV?usp=sharing

Note that the images in the provided bag files are in compressed format. So a decompression command is added at the last line of launch/module_sam.launch. If your own bag records the raw image data, please comment this line out.

drawing drawing

New: more datasets are available at LVI-SAM-Easyused.

Run the package

  1. Configure parameters:
Configure sensor parameters in the .yaml files in the ```config``` folder.
  1. Run the launch file:
roslaunch lvi_sam run.launch
  1. Play existing bag files:
rosbag play handheld.bag

Related Packages

TODO

  • Update graph optimization using all three factors in imuPreintegration.cpp, simplify mapOptimization.cpp, increase system stability

Paper

Thank you for citing our paper if you use any of this code or datasets.

@inproceedings{lvisam2021shan,
  title={LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping},
  author={Shan, Tixiao and Englot, Brendan and Ratti, Carlo and Rus Daniela},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
  pages={5692-5698},
  year={2021},
  organization={IEEE}
}

Acknowledgement

  • The visual-inertial odometry module is adapted from Vins-Mono.
  • The lidar-inertial odometry module is adapted from LIO-SAM.