LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping
LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping
--- ## Dependency - [ROS](http://wiki.ros.org/ROS/Installation) (Tested with kinetic and melodic) - [gtsam](https://gtsam.org/get_started/) (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](https://github.com/ceres-solver/ceres-solver/releases) (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](./docker/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
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.
**New:** more datasets are available at [LVI-SAM-Easyused](https://github.com/Cc19245/LVI-SAM-Easyused). --- ## 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 ``` --- ## Related Packages - [LVI_SAM_fixed by epicjung](https://github.com/epicjung/LVI_SAM_fixed) - [LVI-SAM-modified by skyrim835](https://github.com/skyrim835/LVI-SAM-modified) - [LVI-SAM-Easyused by Cc19245](https://github.com/Cc19245/LVI-SAM-Easyused) --- ## TODO - [ ] Update graph optimization using all three factors in imuPreintegration.cpp, simplify mapOptimization.cpp, increase system stability --- ## Paper Thank you for citing our [paper](./doc/paper.pdf) 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](https://github.com/HKUST-Aerial-Robotics/VINS-Mono). - The lidar-inertial odometry module is adapted from [LIO-SAM](https://github.com/TixiaoShan/LIO-SAM/tree/a246c960e3fca52b989abf888c8cf1fae25b7c25).
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