# hku-mars/r3live

A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package

Repository: https://github.com/hku-mars/r3live
Canonical: https://ross.abutalabs.com/products/r3live
Language: C++
License: GPL-2.0
License Family: copyleft
Topics: slam, lidar-slam, 3d-reconstruction, mesh-reconstruction, sensor-fusion, lidar-inertial-odometry, lidar-camera-fusion, 3d-mapping, lidar-odometry
Last push: 2025-12-04T08:57:54+00:00

## Health v2 (maintenance only)
Score: 48/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 55, release rhythm 8, longevity 100
- inputs: {"age_days": 1819, "days_push": 272, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2442, forks 486 (observed 2026-08-28T04:06:52.463933+00:00)

## What it is
R3LIVE is a tightly-coupled LiDAR-Inertial-Visual sensor fusion framework for robust, real-time state estimation and RGB-colored 3D mapping. It combines a LiDAR-inertial odometry subsystem (FAST-LIO) that builds geometric structure with a visual-inertial odometry subsystem that renders color texture onto the global map.

## Use cases
- build rgb-colored 3d maps from lidar and camera data
- real-time lidar-inertial-visual odometry for a handheld scanner
- state estimation in lidar-degenerated environments
- reconstruct colored meshes of indoor and outdoor scenes
- fuse lidar imu and camera measurements for slam
- generate dense 3d point cloud maps with color texture

## When to choose
- you need tightly-coupled fusion of lidar, imu, and camera for accurate odometry
- you want rgb-colored 3d maps rather than bare geometric point clouds
- your scenario includes lidar-degenerated environments like tunnels or corridors
- you are doing academic research on slam and sensor fusion

## When to avoid
- you only have a camera without lidar or imu sensors
- you need a commercially-licensed solution since the code is GPLv2 with restrictions on commercial use
- you need a lightweight odometry-only solution without mapping
- your platform is not linux/ros based

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, image-processing, graphics, simulation, sdk
- domain: robotics, computer-vision, autonomous-vehicles
- platform: cpp
- tags: slam, lidar-inertial-odometry, visual-inertial-odometry, sensor-fusion, rgb-colored-mapping, 3d-reconstruction, mesh-reconstruction, lidar-camera-fusion, state-estimation, robotics, algorithms, linux, ros

## Member repositories
- hku-mars/r3live (main) score 48

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:52.463933+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:30:41.831307+00:00, confidence not recorded.
  - readme: https://github.com/hku-mars/r3live (fetched 2026-08-28T04:06:52.463933+00:00, sha 6378e0bb72d5)
- Data as of 2026-08-30T08:39:29.467469+00:00.