# hku-mars/FAST_LIO

A computationally efficient and robust LiDAR-inertial odometry (LIO) package

Repository: https://github.com/hku-mars/FAST_LIO
Canonical: https://ross.abutalabs.com/products/fast_lio
Language: C++
License: GPL-2.0
License Family: copyleft
Topics: lidar-odometry, livox-avia-lidar
Last push: 2025-01-15T10:23:34+00:00

## Health v2 (maintenance only)
Score: 33/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 1, release rhythm 35, longevity 100
- inputs: {"age_days": 2151, "days_push": 595, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5099, forks 1669 (observed 2026-08-28T04:09:09.744631+00:00)

## What it is
FAST-LIO is a computationally efficient and robust LiDAR-inertial odometry package that tightly couples LiDAR feature points with IMU data via an iterated extended Kalman filter. Version 2.0 adds incremental mapping with ikd-Tree and direct raw-point odometry supporting spinning and solid-state LiDARs at over 100Hz.

## Use cases
- estimate robot trajectory from LiDAR and IMU data
- build 3D maps in real time on a UAV
- navigate robustly in fast-motion or cluttered environments
- support Livox solid-state and Velodyne/Ouster spinning LiDARs
- use as a LiDAR-inertial front-end for localization or fusion systems

## When to choose
- you need real-time, low-latency LiDAR-inertial odometry on embedded hardware
- your platform uses Livox or common spinning LiDARs with an IMU
- you want a well-established, widely cited SLAM front-end for robotics research

## When to avoid
- you need visual or multi-sensor fusion out of the box (consider FAST-LIVO or R3LIVE)
- you have no IMU data available
- you require a permissive license (it is GPL-2.0)

## Facets
- artifact type: library
- maturity: stable
- function: robotics, computer-vision, simulation
- domain: robotics, autonomous-vehicles
- platform: cpp
- tags: lidar-inertial-odometry, slam, kalman-filter, ikd-tree, livox, point-cloud, algorithms, linux, ros

## Member repositories
- hku-mars/FAST_LIO (main) score 33

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:09.744631+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 2026-08-29T18:02:13.188840+00:00, confidence not recorded.
  - readme: https://github.com/hku-mars/FAST_LIO (fetched 2026-08-28T04:09:09.744631+00:00, sha 7cf0f03461fa)
- Data as of 2026-08-30T08:39:29.467469+00:00.