# superxslam/SuperOdom

A highly robust and accurate LiDAR-only,  LiDAR-inertial odometry

Repository: https://github.com/superxslam/SuperOdom
Canonical: https://ross.abutalabs.com/products/superodom
Homepage: https://superodometry.com
Language: C++
License: GPL-3.0
License Family: copyleft
Topics: 3d-reconstruction, lidar-localization, lidar-odometry, sensor-fusion, resilient-slam, uncertianty-estimation
Last push: 2026-07-23T17:37:20+00:00

## Health v2 (maintenance only)
Score: 52/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 94, release rhythm 8, longevity 36
- inputs: {"age_days": 510, "days_push": 41, "days_rel": 495, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1101, forks 141 (observed 2026-08-28T04:03:35.534210+00:00)

## What it is
SuperOdometry is a lightweight C++/ROS library for LiDAR-only and LiDAR-inertial odometry and mapping, developed by CMU's AirLab. It fuses LiDAR and IMU pose estimates bidirectionally, supports Livox, Velodyne, and Ouster sensors, and offers degeneracy awareness, alignment risk prediction, and localization/mapping dual modes.

## Use cases
- estimate robot pose from lidar and imu data
- lidar-inertial odometry for drones and legged robots
- build 3d maps from lidar scans
- localize a robot on a prior map
- run odometry without an imu using lidar only
- detect degeneracy in icp alignment
- support livox velodyne and ouster lidars

## When to choose
- you need robust, accurate real-time odometry from LiDAR with optional IMU fusion
- you work with Livox, Velodyne, or Ouster sensors in ROS/ROS2
- you need degeneracy detection and uncertainty estimation for state estimation
- you want a research-proven system (Science Robotics) with active maintenance and community support

## When to avoid
- you need visual or visual-inertial SLAM with camera support
- you need a plug-and-play solution without ROS or C++ build tooling
- your project requires a permissive license (GPL-3.0)
- you only need simple GPS-based localization without LiDAR

## Facets
- artifact type: library
- maturity: active
- function: robotics, computer-vision, simulation
- domain: robotics, autonomous-vehicles, computer-vision
- platform: cpp
- tags: lidar-odometry, imu-fusion, slam, state-estimation, mapping, localization, ros2, livox, velodyne, ouster, degeneracy-detection, icp, sensor-fusion, algorithms, linux, ros, docker

## Member repositories
- superxslam/SuperOdom (main) score 52

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:35.534210+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 2026-08-30T06:45:44.342590+00:00, confidence not recorded.
  - readme: https://github.com/superxslam/SuperOdom (fetched 2026-08-28T04:03:35.534210+00:00, sha 66e4613958dc)
  - homepage: https://superodometry.com (fetched 2026-08-29T12:48:42.782018+00:00, sha c96581e5ccfe)
- Data as of 2026-08-30T08:39:29.467469+00:00.