Slayer-swjtu / LVI-SAM

LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping

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.

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Dependency

• ROS (Tested with kinetic and melodic)
• 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 -j4
  

• 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
  

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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

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Datasets

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.

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Run the package

1. Configure parameters:

Configure sensor parameters in the .yaml files in the ```config``` folder.

2. Run the launch file:

roslaunch lvi_sam run.launch

3. Play existing bag files:

rosbag play handheld.bag 

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TODO

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

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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={to-be-added},
  year={2021},
  organization={IEEE}
}

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Acknowledgement

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