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 -------------------
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Removed the feature extraction module, making it easier to adapt to different lidars;
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Support 'robosense' lidar and Mulran datasets, make the following changes in "*.yaml":
- sensor: “robosense” or sensor: “mulran”
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Support 6-axis IMU, make the following changes in "*.yaml":
- imuType: 0 # 0: 6-axis IMU, 1: 9-axis IMU
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Support low frequency IMU（50HZ、100HZ）, make the following changes in "*.yaml":
- imuRate: 500
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Re-derivation the LM optimization, don't need coordinate transformation;
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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).
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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;
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The original version of lvi-sam dose not consider the translation amount between lidar and camera, this version adds the translation amount;
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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 closure -
Support UrbanNav datasets;
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Support M2DGR datasets;
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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 -j8
- Run the launch file:
roslaunch lviorf run_kitti.launch
- Play bag files:
rosbag play kitti_2011_09_30_drive_0018_synced.bag
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Make 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;
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Modify 'gpsTopic' paramter in '*.yaml' with yourself gnss topic;
gpsTopic: "gps/fix" # GPS topic -
If 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;
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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:
- *_camera.yaml
imu_topic: "/imu/data" # IMU Topic
image_topic: "/camera/image_color" # Camera Topic
# camera model
model_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
#imu parameters The more accurate parameters you provide, the worse performance
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.
#Rotation from camera frame to imu frame, imu^R_cam
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]
#Translation from camera frame to imu frame, imu^T_cam
extrinsicTranslation: !!opencv-matrix
rows: 3
cols: 1
dt: d
data: [-1.7434527332030676e-02, 1.7171139776467173e-01, -4.5251036141047592e-02]
- *_lidar.yaml
pointCloudTopic: "/velodyne_points" # Lidar Topic
imuTopic: "/imu/data" # IMU Topic
# Sensor Settings
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)
# IMU Settings
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
# Extrinsics: T_lb (lidar -> imu)
extrinsicTrans: [0.0, 0.0, 0.0]
extrinsicRot: [1, 0, 0,
0, 1, 0,
0, 0, 1]
# This parameter is set only when the 9-axis IMU is used, but it must be a high-precision IMU. e.g. MTI-680
extrinsicRPY: [0, -1, 0,
1, 0, 0,
0, 0, 1]
- lvi-sam dataset
- Kitti 05
- Add performance comparison;
Thanks for LVI-SAM, FAST_LIO2, M2DGR and UrbanNavDataset.
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.
- ROS (Tested with kinetic and melodic)
- gtsam (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 (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
When you use Docker, you could solve the dependency at once.
For more information, you can check docker_start.md.
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
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.
- 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
- Update graph optimization using all three factors in imuPreintegration.cpp, simplify mapOptimization.cpp, increase system stability
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={5692-5698},
year={2021},
organization={IEEE}
}