# gaoxiang12/lightning-lm

Lidar Localization and Mapping

Repository: https://github.com/gaoxiang12/lightning-lm
Canonical: https://ross.abutalabs.com/products/lightning-lm
Language: C++
License Family: other
Last push: 2026-04-09T10:15:45+00:00

## Health v2 (maintenance only)
Score: 51/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 76, release rhythm 35, longevity 22
- inputs: {"age_days": 308, "days_push": 146, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1632, forks 330 (observed 2026-08-28T04:05:14.155976+00:00)

## What it is
Lightning-LM is a C++ library providing a complete 3D LiDAR SLAM system with fast LIO front-end, real-time loop closure detection, and high-precision LiDAR localization. It includes 3D-to-2D map conversion, dynamic map partition loading, and a lightweight incremental optimization library, all running efficiently on CPU.

## Use cases
- build a 3D lidar SLAM system for a robot
- localize a vehicle with lidar in real time
- generate 2D grid maps from 3D lidar scans
- run lidar-inertial odometry on low-power CPU hardware
- handle dynamic scenes with separate static and dynamic map layers
- detect and correct loop closures in mapping
- map multi-floor indoor environments

## When to choose
- you need a complete, efficient LiDAR mapping and localization stack in C++
- you want real-time loop closure and localization under one CPU core
- you need 2D grid map output from 3D LiDAR data
- you work with large-scale scenes requiring dynamic map partition loading

## When to avoid
- you need visual or camera-based SLAM rather than LiDAR
- you need GPS or wheel odometry fusion, which is not yet implemented
- you require a permissively licensed project - no license is specified
- you want a plug-and-play ROS package with extensive documentation

## Facets
- artifact type: library
- maturity: active
- function: simulation, computer-vision, math, concurrency
- domain: robotics, autonomous-vehicles
- platform: cpp, cross-platform
- tags: slam, lidar, lio, localization, mapping, point-cloud, eskf, loop-closure, imu, robotics-perception, algorithms, linux

## Member repositories
- gaoxiang12/lightning-lm (main) score 51

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:14.155976+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 2026-08-30T03:47:47.163055+00:00, confidence not recorded.
  - readme: https://github.com/gaoxiang12/lightning-lm (fetched 2026-08-28T04:05:14.155976+00:00, sha 1bac1e44bff4)
- Data as of 2026-08-30T08:39:29.467469+00:00.