https://github.com/TixiaoShan/LVI-SAM
LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping
https://github.com/TixiaoShan/LVI-SAM
lidar-odometry visual-odometry
Last synced: 3 months ago
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LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping
- Host: GitHub
- URL: https://github.com/TixiaoShan/LVI-SAM
- Owner: TixiaoShan
- License: bsd-3-clause
- Created: 2021-03-04T02:26:05.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2022-12-17T15:34:36.000Z (over 2 years ago)
- Last Synced: 2025-03-18T01:40:45.974Z (3 months ago)
- Topics: lidar-odometry, visual-odometry
- Language: C++
- Homepage: https://youtu.be/8CTl07D6Ibc
- Size: 51.9 MB
- Stars: 1,586
- Watchers: 34
- Forks: 475
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# LVI-SAM
This repository contains code for a lidar-visual-inertial odometry and mapping system, which combines the advantages of [LIO-SAM](https://github.com/TixiaoShan/LIO-SAM/tree/a246c960e3fca52b989abf888c8cf1fae25b7c25) and [Vins-Mono](https://github.com/HKUST-Aerial-Robotics/VINS-Mono) at a system level.
---
## 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).