FAST-LIVO2

hku-marsGPL-2.0更新放缓

面向机器人与无人机的激光雷达-惯性-视觉紧耦合里程计,实时输出高精度位姿与彩色点云,支撑三维重建与SLAM。

FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry

暂无版本4,866star +18 / 7 天890fork同步于 2026-11-11 11:36 UTC

怎么上手

由 README 提炼,执行前请确认
cd ~/catkin_ws/src
git clone https://github.com/hku-mars/FAST-LIVO2
cd ../
catkin_make
source ~/catkin_ws/devel/setup.bash
硬件要求按 README 说明

README 未说明具体显存或 GPU 型号要求;软件环境要求 Ubuntu 18.04~20.04 与 ROS。

下载:源码 zip·GitHub Releases(安装包)本站不镜像安装包

项目说明

由 README 自动翻译提炼

激光-惯性-视觉紧耦合里程计,在退化环境下做实时定位与建图

面向机器人与无人机的激光雷达-惯性-视觉紧耦合里程计,实时输出高精度位姿与彩色点云,支撑三维重建与SLAM。

核心能力

  • 紧耦合融合 LiDAR、IMU 与相机做直接法里程计
  • 面向严重退化环境提供实时三维重建与机载定位
  • 开源硬同步手持设备方案含 CAD 与 STM32 源码
  • 配套 FAST-LIVO2-Dataset 与 FAST-Calib 外参标定工具

容易踩的坑

需先手动安装 Sophus 并切到 a621ff 提交,且 Vikit 要用 rpg_vikit 而非 fast-livo1 的版本;运行示例需自行下载 FAST-LIVO2-Dataset 数据集;依赖 PCL>=1.8、Eigen>=3.3.4、OpenCV>=4.2,版本不足会编译失败。

内容由模型根据 README 生成,可能不完整或不准确,以官方文档为准。看原文 README

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    README 原文

    未翻译

    FAST-LIVO2

    FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry

    📢 News

    • 🔓 2025-01-23: Code released!
    • 🎉 2024-10-01: Accepted by T-RO '24!
    • 🚀 2024-07-02: Conditionally accepted.

    📬 Contact

    For further inquiries or assistance, please contact [email protected].

    1. Introduction

    FAST-LIVO2 is an efficient and accurate LiDAR-inertial-visual fusion localization and mapping system, demonstrating significant potential for real-time 3D reconstruction and onboard robotic localization in severely degraded environments.

    Developer: Chunran Zheng 郑纯然

    1.1 Related video

    Our accompanying video is now available on Bilibili and YouTube.

    1.2 Related paper

    FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry

    FAST-LIVO2 on Resource-Constrained Platforms

    FAST-LIVO: Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometry

    FAST-Calib: LiDAR-Camera Extrinsic Calibration in One Second

    1.3 Our hard-synchronized equipment

    We open-source our handheld device, including CAD files, synchronization scheme, STM32 source code, wiring instructions, and sensor ROS driver. Access these resources at this repository: LIV_handhold.

    1.4 Our associate dataset: FAST-LIVO2-Dataset

    Our associate dataset FAST-LIVO2-Dataset used for evaluation is also available online.

    1.5 Our LiDAR-camera calibration method

    The FAST-Calib toolkit is recommended. Its output extrinsic parameters can be directly filled into the YAML file.

    2. Prerequisited

    2.1 Ubuntu and ROS

    Ubuntu 18.04~20.04. ROS Installation.

    2.2 PCL && Eigen && OpenCV

    PCL>=1.8, Follow PCL Installation.

    Eigen>=3.3.4, Follow Eigen Installation.

    OpenCV>=4.2, Follow Opencv Installation.

    2.3 Sophus

    Sophus Installation for the non-templated/double-only version.

    git clone https://github.com/strasdat/Sophus.git
    cd Sophus
    git checkout a621ff
    mkdir build && cd build && cmake ..
    make
    sudo make install
    

    2.4 Vikit

    Vikit contains camera models, some math and interpolation functions that we need. Vikit is a catkin project, therefore, download it into your catkin workspace source folder.

    # Different from the one used in fast-livo1
    cd catkin_ws/src
    git clone https://github.com/xuankuzcr/rpg_vikit.git 
    

    3. Build

    Clone the repository and catkin_make:

    cd ~/catkin_ws/src
    git clone https://github.com/hku-mars/FAST-LIVO2
    cd ../
    catkin_make
    source ~/catkin_ws/devel/setup.bash
    

    4. Run our examples

    Download FAST-LIVO2-Dataset from Global-LVBA Section IV.

    roslaunch fast_livo mapping_avia.launch
    rosbag play YOUR_DOWNLOADED.bag
    

    5. License

    The source code of this package is released under the GPLv2 license. For commercial use, please contact me at [email protected] and Prof. Fu Zhang at [email protected] to discuss an alternative license.