# TixiaoShan/LIO-SAM

LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping

Repository: https://github.com/TixiaoShan/LIO-SAM
Canonical: https://ross.abutalabs.com/products/lio-sam
Language: C++
License: BSD-3-Clause
License Family: permissive
Topics: lidar-odometry, lidar-slam, loam-velodyne, lidar-inertial, 3d-mapping, slam, ouster-slam, velodyne-slam, ouster, velodyne
Last push: 2025-02-14T10:05:51+00:00

## Health v2 (maintenance only)
Score: 35/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 6, release rhythm 35, longevity 100
- inputs: {"age_days": 2254, "days_push": 565, "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 4895, forks 1528 (observed 2026-08-28T04:09:02.041510+00:00)

## What it is
LIO-SAM is a real-time tightly-coupled lidar-inertial odometry and mapping package built on factor graph optimization with GTSAM. It fuses lidar and IMU data to produce high-accuracy 3D odometry and mapping, running up to 10x faster than real time.

## Use cases
- estimate robot pose from lidar and IMU data
- build 3D maps with a Velodyne or Ouster lidar
- run real-time SLAM on a mobile robot or handheld device
- fuse GPS factors with lidar odometry
- localize a robot in a previously mapped environment

## When to choose
- you need accurate real-time lidar-inertial odometry in ROS
- you have a supported lidar (Velodyne, Ouster, Livox) with a 9-axis IMU
- you want a well-cited, widely used SLAM baseline for research or deployment

## When to avoid
- you have no IMU or only low-rate IMU data
- you need visual or visual-inertial SLAM with cameras
- you are not using ROS and don't want to adapt the code

## Facets
- artifact type: library
- maturity: stable
- function: robotics, simulation, computer-vision, math
- domain: robotics, autonomous-vehicles
- platform: cpp
- tags: slam, lidar-odometry, lidar-inertial, factor-graph-optimization, gtsam, ros, 3d-mapping, velodyne, ouster, algorithms, linux

## Member repositories
- TixiaoShan/LIO-SAM (main) score 35

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:02.041510+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 2026-08-29T18:18:10.331395+00:00, confidence not recorded.
  - readme: https://github.com/TixiaoShan/LIO-SAM (fetched 2026-08-28T04:09:02.041510+00:00, sha c88902f7658a)
- Data as of 2026-08-30T08:39:29.467469+00:00.