Lviorf is a lidar-visual-inertial odometry and mapping system, which modified based on lvi-sam and liorf. The lviorf has the following new features:
------------------- Update Date: 2023-02-06 -------------------
Removed the feature extraction module, making it easier to adapt to different lidars;
Support 'robosense' lidar and Mulran datasets, make the following changes in "*.yaml":
Support 6-axis IMU, make the following changes in "*.yaml":
Support low frequency IMU（50HZ、100HZ）, make the following changes in "*.yaml":
Re-derivation the LM optimization, don't need coordinate transformation;
Modified gps factor, no longer depending on the 'robot_localization' package, and make it easier to adapt to different gnss device(RTK/GPS). In theoretically, lviorf is easier to adapt to gnss devices with different frequencies(10HZ~500HZ).
The original version of lvi-sam sets many sensor external parameters to fixed values in the code, lviorf extracts the external parameters into the yaml file, making it easier to configure;
The original version of lvi-sam dose not consider the translation amount between lidar and camera, this version adds the translation amount;
In the lio system, you can choose whether to add visual loop closure constraint by changing the yaml file:
visualLoopClosureEnableFlag: true # visual loop closure
loopClosureEnableFlag: true # RS loop closureSupport UrbanNav datasets;
Support M2DGR datasets;
Support kitti datasets.
Blog：LVI-SAM：配置环境、安装测试、适配自己采集数据集
Video：基于LIO-SAM框架SLAM算法开发（八）：建图之雷达视觉惯性融合建图-lviorf
compile package
mkdir -p ~/lviorf/src
cd ~/lviorf/src
git clone https://github.com/YJZLuckyBoy/lviorf.git
cd ..
catkin_make -j8Run the launch file:
roslaunch lviorf run_kitti.launchPlay bag files:
rosbag play kitti_2011_09_30_drive_0018_synced.bagMake sure your gnss topic type is 'sensor_msgs::NavSatFix'. If the gps data contains covariance information, ensure that only the data with small covariance is used;
Modify 'gpsTopic' paramter in '*.yaml' with yourself gnss topic;
gpsTopic: "gps/fix" # GPS topicIf you want to use lviorf with integrated gps factor in kitti dataset, you can use the modified python script in "config/doc/kitti2bag" to obtain high-frequency gps data(Rate: 100HZ, Topic: '/gps/fix/correct'). About how to use "kitti2bag.py", please refer to doc/kitti2bag;
The synchronized bag can be downloaded from Google Driver. kitti_2011_09_30_drive_0018_synced.bag
With lviorf, you can easily run your own dataset, and only modify the following parameters for the yaml file:
imu_topic: "/imu/data" # IMU Topic
image_topic: "/camera/image_color" # Camera Topicmodel_type: PINHOLE camera_name: camera image_width: 1920 image_height: 1200 distortion_parameters: k1: -0.109203 k2: 0.063536 p1: -0.003427 p2: -0.000629 projection_parameters: fx: 1086.160899 fy: 1090.242963 cx: 940.067502 cy: 586.740077
acc_n: 8.1330537434371481e-03 # accelerometer measurement noise standard deviation. gyr_n: 7.4266825125507141e-03 # gyroscope measurement noise standard deviation. acc_w: 1.2123362494392119e-04 # accelerometer bias random work noise standard deviation. gyr_w: 8.6572985145653080e-05 # gyroscope bias random work noise standard deviation.
extrinsicRotation: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 9.9934608718980233e-01, -1.5715484428488590e-02,-3.2564114721728155e-02, 3.2359037356803094e-02, -1.3131917124154624e-02,9.9939003669937865e-01, -1.6133527815482926e-02, -9.9979026615676858e-01,-1.2614792047622947e-02]
extrinsicTranslation: !!opencv-matrix rows: 3 cols: 1 dt: d data: [-1.7434527332030676e-02, 1.7171139776467173e-01, -4.5251036141047592e-02]
2. *_lidar.yamlpointCloudTopic: "/velodyne_points" # Lidar Topic imuTopic: "/imu/data" # IMU Topic
sensor: velodyne # lidar sensor type, 'velodyne' or 'ouster' or 'livox' or 'robosense' N_SCAN: 32 # number of lidar channel (i.e., Velodyne/Ouster: 16, 32, 64, 128, Livox Horizon: 6) Horizon_SCAN: 2000 # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048, Livox Horizon: 4000)
imuType: 0 # 0: 6-axis 1: 9-axis imuRate: 100.0 imuAccNoise: 8.1330537434371481e-03 imuGyrNoise: 7.4266825125507141e-03 imuAccBiasN: 1.2123362494392119e-04 imuGyrBiasN: 8.6572985145653080e-05
extrinsicTrans: [0.0, 0.0, 0.0] extrinsicRot: [1, 0, 0, 0, 1, 0, 0, 0, 1]
extrinsicRPY: [0, -1, 0, 1, 0, 0, 0, 0, 1]
## Mapping
1. lvi-sam dataset
<p align='center'>
<img src="https://github.com/YJZLuckyBoy/pic/blob/master/lviorf/lvi-sam/lvi-sam-dataset.gif" alt="drawing" width="800" height = "400"/>
</p>
2. kitti-05 dataset
<p align='center'>
<img src="https://github.com/YJZLuckyBoy/pic/blob/master/lviorf/Kitti/Kitti05.gif" alt="drawing" width="800" height = "400"/>
</p>
3. UrbanNav dataset
<p align='center'>
<img src="https://github.com/YJZLuckyBoy/pic/blob/master/lviorf/UrbanNav/UrbanNavDataset.gif" alt="drawing" width="800" height = "400"/>
</p>
4. M2DGR dataset
<p align='center'>
<img src="https://github.com/YJZLuckyBoy/pic/blob/master/lviorf/M2DGR/gate_01.gif" alt="drawing" width="800" height = "400"/>
</p>
5. R3live dataset
<p align='center'>
<img src="https://github.com/YJZLuckyBoy/pic/blob/master/lviorf/R3live/hku_main_building.gif" alt="drawing" width="800" height = "400"/>
</p>
6. My dataset
<p align='center'>
<img src="https://github.com/YJZLuckyBoy/pic/blob/master/lviorf/Mydata/device00.png" alt="drawing" width="400"/>
<img src="https://github.com/YJZLuckyBoy/pic/blob/master/lviorf/Mydata/device01.png" alt="drawing" width="400"/>
</p>
## Performance
1. Kitti 05
## TODO
- [ ] Add performance comparison;
## Acknowledgments
Thanks for [LVI-SAM](https://github.com/TixiaoShan/LVI-SAM), [FAST_LIO2](https://github.com/hku-mars/FAST_LIO), [M2DGR](https://github.com/SJTU-ViSYS/M2DGR) and [UrbanNavDataset](https://github.com/weisongwen/UrbanNavDataset).
# 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.
<p align='center'>
<img src="https://github.com/YJZLuckyBoy/lviorf/raw/master/doc/demo.gif" alt="drawing" width="800"/>
</p>
---
## 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
<p align='center'>
<img src="https://github.com/YJZLuckyBoy/lviorf/raw/master/doc/sensor.jpeg" alt="drawing" width="600"/>
</p>
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.
<p align='center'>
<img src="https://github.com/YJZLuckyBoy/lviorf/raw/master/doc/jackal-earth.png" alt="drawing" width="286.5"/>
<img src="https://github.com/YJZLuckyBoy/lviorf/raw/master/doc/handheld-earth.png" alt="drawing" width="328"/>
</p>
---
## 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)
---
## 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).