视觉激光雷达融合代码

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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.

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

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

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.

Run the package

Configure parameters:

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

Run the launch file:
```
roslaunch lvi_sam run.launch
```
Play existing bag files:
```
rosbag play handheld.bag 
```

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={to-be-added},
  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.

README.md