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FAST LIVO2

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

From hku-marsยทUpdated June 16, 2026ยทView on GitHubยท

- ๐Ÿ”“ **2025-01-23**: Code released! - ๐ŸŽ‰ **2024-10-01**: Accepted by **T-RO '24**! - ๐Ÿš€ **2024-07-02**: Conditionally accepted. The project is written primarily in C++, distributed under the GNU General Public License v2.0 license, first published in 2024. It has gained significant community traction with 4,207 stars and 757 forks on GitHub. Key topics include: 3d-reconstruction, colored-point-cloud, gaussian-splatting, lidar-camera-fusion, lidar-inertial-odometry.

FAST-LIVO2

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

๐Ÿ“ข News

  • ๐Ÿ”“ 2025-01-23: Code released!
  • ๐ŸŽ‰ 2024-10-01: Accepted by T-RO '24!
  • ๐Ÿš€ 2024-07-02: Conditionally accepted.

๐Ÿ“ฌ Contact

For further inquiries or assistance, please contact zhengcr@connect.hku.hk.

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 ้ƒ‘็บฏ็„ถ

<div align="center"> <img src="pics/Framework.png" width = 100% > </div>

Our accompanying video is now available on Bilibili and YouTube.

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.

bash
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.

bash
# 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 zhengcr@connect.hku.hk and Prof. Fu Zhang at fuzhang@hku.hk to discuss an alternative license.

Contributors

Showing top 1 contributor by commit count.

View all contributors on GitHub โ†’

This article is auto-generated from hku-mars/FAST-LIVO2 via the GitHub API.Last fetched: 6/16/2026