# engcang/SLAM-application

LeGO-LOAM, LIO-SAM, LVI-SAM, FAST-LIO2, Faster-LIO, VoxelMap, R3LIVE, Point-LIO, KISS-ICP, DLO, DLIO, Ada-LIO, PV-LIO, SLAMesh, ImMesh, FAST-LIO-MULTI, M-LOAM, LOCUS, SLICT, MA-LIO, CT-ICP, GenZ-ICP, iG-LIO, SR-LIO application and comparison on Gazebo and real-world datasets. Installation and config files are provided.

Repository: https://github.com/engcang/SLAM-application
Canonical: https://ross.abutalabs.com/products/slam-application
Language: C++
License: BSD-3-Clause
License Family: permissive
Topics: ros, slam, robotics, lidar, lidar-inertial-odometry, lidar-odometry, multi-lidar-inertial-odometry
Last push: 2025-01-31T19:56:04+00:00

## Health v2 (maintenance only)
Score: 34/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 4, release rhythm 35, longevity 100
- inputs: {"age_days": 1946, "days_push": 579, "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 1206, forks 157 (observed 2026-08-28T04:03:59.385287+00:00)

## What it is
A curated collection of installation guides, configuration files, and comparison results for many open-source LiDAR(-inertial) SLAM and odometry algorithms, tested on Gazebo simulation and real-world datasets. It bundles 20+ algorithms such as FAST-LIO2, LIO-SAM, and KISS-ICP with demo videos comparing their performance.

## Use cases
- compare lidar slam algorithms before choosing one
- install and configure fast-lio2 or lio-sam on ros
- evaluate lidar inertial odometry on gazebo datasets
- find which slam method works in narrow tunnels or stairs
- set up multi-lidar odometry like fast-lio-multi
- learn slam algorithm differences with demo videos

## When to choose
- you want to benchmark or compare existing SLAM/odometry algorithms side by side
- you need ready-made install and config files for many LiDAR SLAM packages
- you are evaluating which odometry approach fits your sensor setup (single/multi LiDAR, with/without IMU)

## When to avoid
- you need a production-ready SLAM library itself - this is a comparison and setup guide, not an algorithm implementation
- you work with visual-only or 2D SLAM
- you need a non-ROS or Windows environment

## Facets
- artifact type: learning-resource
- maturity: active
- function: simulation, robotics, benchmarking
- domain: robotics, autonomous-vehicles, simulation, tutorials
- platform: cpp
- tags: slam, lidar, lidar-inertial-odometry, gazebo, ros, comparison, point-cloud, linux

## Member repositories
- engcang/SLAM-application (main) score 34

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:59.385287+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 2026-08-30T06:19:17.575058+00:00, confidence not recorded.
  - readme: https://github.com/engcang/SLAM-application (fetched 2026-08-28T04:03:59.385287+00:00, sha 70ea024c6ba5)
- Data as of 2026-08-30T08:39:29.467469+00:00.