# hyye/lio-mapping

Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping)

Repository: https://github.com/hyye/lio-mapping
Canonical: https://ross.abutalabs.com/products/lio-mapping
Homepage: https://sites.google.com/view/lio-mapping
Language: C++
License: GPL-3.0
License Family: copyleft
Topics: sensor-fusion, slam, mapping, icra2019
Last push: 2020-02-13T20:05:15+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2907, "days_push": 2393, "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 1028, forks 324 (observed 2026-08-28T04:03:17.431520+00:00)

## What it is
A C++/ROS implementation of tightly coupled 3D lidar-inertial odometry and mapping (LIO-mapping), published at ICRA 2019. It jointly optimizes lidar and IMU measurements to estimate sensor poses at IMU update rate and refine them against a global map.

## Use cases
- estimate robot trajectory from lidar and IMU data
- build 3D maps from spinning lidar point clouds
- run lidar-inertial odometry on rosbag recordings
- reproduce ICRA 2019 lidar-inertial fusion results
- compare SLAM odometry methods against LOAM and VINS-mono baselines

## When to choose
- you need tightly coupled lidar-IMU fusion with high-rate pose estimation
- you work in ROS with Velodyne-style 3D lidar and want a research-grade odometry pipeline
- you want a reference implementation of the LIO-mapping paper for academic comparison

## When to avoid
- you need a maintained project with recent updates or active community support
- you want visual-inertial or lidar-visual-inertial SLAM rather than lidar-IMU only
- you are not using ROS or Linux, since the build depends on ROS, Ceres, PCL, and OpenCV

## Facets
- artifact type: library
- maturity: maintenance
- function: simulation, computer-vision, machine-learning
- domain: robotics, autonomous-vehicles
- platform: cpp
- tags: slam, lidar, sensor-fusion, imu, odometry, mapping, point-cloud, ceres-solver, ros, icra-2019, algorithms, linux, docker

## Member repositories
- hyye/lio-mapping (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:17.431520+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 2026-08-30T07:07:46.454418+00:00, confidence not recorded.
  - readme: https://github.com/hyye/lio-mapping (fetched 2026-08-28T04:03:17.431520+00:00, sha eede88da81e6)
  - homepage: https://sites.google.com/view/lio-mapping (fetched 2026-08-29T13:07:39.118704+00:00, sha 42ef3554692d)
- Data as of 2026-08-30T08:39:29.467469+00:00.