{"id":13436305,"url":"https://github.com/TixiaoShan/LVI-SAM","last_synced_at":"2025-03-18T21:30:18.119Z","repository":{"id":39257665,"uuid":"344324850","full_name":"TixiaoShan/LVI-SAM","owner":"TixiaoShan","description":"LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping","archived":false,"fork":false,"pushed_at":"2022-12-17T15:34:36.000Z","size":54391,"stargazers_count":1586,"open_issues_count":10,"forks_count":475,"subscribers_count":34,"default_branch":"master","last_synced_at":"2025-03-18T01:40:45.974Z","etag":null,"topics":["lidar-odometry","visual-odometry"],"latest_commit_sha":null,"homepage":"https://youtu.be/8CTl07D6Ibc","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/TixiaoShan.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-03-04T02:26:05.000Z","updated_at":"2025-03-14T21:56:27.000Z","dependencies_parsed_at":"2023-01-29T17:31:03.302Z","dependency_job_id":null,"html_url":"https://github.com/TixiaoShan/LVI-SAM","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TixiaoShan%2FLVI-SAM","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TixiaoShan%2FLVI-SAM/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TixiaoShan%2FLVI-SAM/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TixiaoShan%2FLVI-SAM/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/TixiaoShan","download_url":"https://codeload.github.com/TixiaoShan/LVI-SAM/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244310240,"owners_count":20432500,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["lidar-odometry","visual-odometry"],"created_at":"2024-07-31T03:00:46.489Z","updated_at":"2025-03-18T21:30:18.103Z","avatar_url":"https://github.com/TixiaoShan.png","language":"C++","funding_links":[],"categories":["C++","5 Localization and SLAM","五、Code"],"sub_categories":["3.4 High Performance Inference","4. Lidar SLAM"],"readme":"# LVI-SAM\n\nThis 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.\n\n\u003cp align='center'\u003e\n    \u003cimg src=\"./doc/demo.gif\" alt=\"drawing\" width=\"800\"/\u003e\n\u003c/p\u003e\n\n---\n\n## Dependency\n\n- [ROS](http://wiki.ros.org/ROS/Installation) (Tested with kinetic and melodic)\n- [gtsam](https://gtsam.org/get_started/) (Georgia Tech Smoothing and Mapping library)\n  ```\n  sudo add-apt-repository ppa:borglab/gtsam-release-4.0\n  sudo apt install libgtsam-dev libgtsam-unstable-dev\n  ```\n- [Ceres](https://github.com/ceres-solver/ceres-solver/releases) (C++ library for modeling and solving large, complicated optimization problems)\n  ```\n  sudo apt-get install -y libgoogle-glog-dev\n  sudo apt-get install -y libatlas-base-dev\n  wget -O ~/Downloads/ceres.zip https://github.com/ceres-solver/ceres-solver/archive/1.14.0.zip\n  cd ~/Downloads/ \u0026\u0026 unzip ceres.zip -d ~/Downloads/\n  cd ~/Downloads/ceres-solver-1.14.0\n  mkdir ceres-bin \u0026\u0026 cd ceres-bin\n  cmake ..\n  sudo make install -j4\n  ```\n\n### Getting start with Docker  \n\nWhen you use Docker, you could solve the dependency at once.  \nFor more information, you can check [docker_start.md](./docker/docker_start.md).    \n\n---\n\n## Compile\n\nYou can use the following commands to download and compile the package.\n\n```\ncd ~/catkin_ws/src\ngit clone https://github.com/TixiaoShan/LVI-SAM.git\ncd ..\ncatkin_make\n```\n\n---\n\n## Datasets\n\n\u003cp align='center'\u003e\n    \u003cimg src=\"./doc/sensor.jpeg\" alt=\"drawing\" width=\"600\"/\u003e\n\u003c/p\u003e\n\nThe 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.\n\n```\nhttps://drive.google.com/drive/folders/1q2NZnsgNmezFemoxhHnrDnp1JV_bqrgV?usp=sharing\n```\n\n**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.\n\n\u003cp align='center'\u003e\n    \u003cimg src=\"./doc/jackal-earth.png\" alt=\"drawing\" width=\"286.5\"/\u003e\n    \u003cimg src=\"./doc/handheld-earth.png\" alt=\"drawing\" width=\"328\"/\u003e\n\u003c/p\u003e\n\n**New:** more datasets are available at [LVI-SAM-Easyused](https://github.com/Cc19245/LVI-SAM-Easyused).\n\n---\n\n## Run the package\n\n1. Configure parameters:\n\n```\nConfigure sensor parameters in the .yaml files in the ```config``` folder.\n```\n\n2. Run the launch file:\n```\nroslaunch lvi_sam run.launch\n```\n\n3. Play existing bag files:\n```\nrosbag play handheld.bag \n```\n\n---\n\n## Related Packages\n\n  - [LVI_SAM_fixed by epicjung](https://github.com/epicjung/LVI_SAM_fixed)\n  - [LVI-SAM-modified by skyrim835](https://github.com/skyrim835/LVI-SAM-modified)\n  - [LVI-SAM-Easyused by Cc19245](https://github.com/Cc19245/LVI-SAM-Easyused)\n\n---\n\n## TODO\n\n  - [ ] Update graph optimization using all three factors in imuPreintegration.cpp, simplify mapOptimization.cpp, increase system stability \n\n---\n\n## Paper \n\nThank you for citing our [paper](./doc/paper.pdf) if you use any of this code or datasets.\n\n```\n@inproceedings{lvisam2021shan,\n  title={LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping},\n  author={Shan, Tixiao and Englot, Brendan and Ratti, Carlo and Rus Daniela},\n  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},\n  pages={5692-5698},\n  year={2021},\n  organization={IEEE}\n}\n```\n\n---\n\n## Acknowledgement\n\n  - The visual-inertial odometry module is adapted from [Vins-Mono](https://github.com/HKUST-Aerial-Robotics/VINS-Mono).\n  - The lidar-inertial odometry module is adapted from [LIO-SAM](https://github.com/TixiaoShan/LIO-SAM/tree/a246c960e3fca52b989abf888c8cf1fae25b7c25).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FTixiaoShan%2FLVI-SAM","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FTixiaoShan%2FLVI-SAM","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FTixiaoShan%2FLVI-SAM/lists"}