# hku-mars/Point-LIO

High-Bandwidth LiDAR Inertial Odometry

Repository: https://github.com/hku-mars/Point-LIO
Canonical: https://ross.abutalabs.com/products/point-lio
Language: C++
License: NOASSERTION
License Family: other
Topics: efficient, high-bandwidth, lio, odometry
Last push: 2026-06-13T13:09:52+00:00

## Health v2 (maintenance only)
Score: 71/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 87, release rhythm 35, longevity 97
- inputs: {"age_days": 1364, "days_push": 81, "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 1318, forks 213 (observed 2026-08-28T04:04:21.156683+00:00)

## What it is
Point-LIO is a robust high-bandwidth LiDAR-inertial odometry framework that estimates ego-motion and builds maps by fusing LiDAR point clouds with IMU data. It outputs odometry at very high frequencies (4k-8kHz) and remains robust under aggressive motions, IMU saturation, and severe vibration.

## Use cases
- estimate robot trajectory from LiDAR and IMU data
- build 3D maps while a drone or robot moves aggressively
- get high-frequency odometry for real-time feedback control
- run odometry on datasets with fast rotations and severe vibration
- perform LiDAR-inertial odometry without an IMU using a gravity prior
- support trajectory planning and perception with distortion-free point clouds

## When to choose
- your platform experiences high angular/linear velocities or vibration that breaks standard LIO methods
- you need odometry output at kHz rates for control or perception loops
- your IMU saturates during aggressive motion and you need robustness to that
- you want per-point processing to avoid motion distortion in mapping

## When to avoid
- you need a full SLAM system with loop closure rather than pure odometry
- your LiDAR point clouds lack per-point timestamps, which Point-LIO requires
- you need a plug-and-play solution without tuning IMU saturation and extrinsic parameters
- you work outside ROS since the tooling is ROS-centric

## Facets
- artifact type: library
- maturity: active
- function: robotics, simulation, computer-vision
- domain: robotics, autonomous-vehicles, computer-vision
- platform: cpp
- tags: lidar, odometry, slam, imu, point-cloud, state-estimation, kalman-filter, ros, sensor-fusion, algorithms, linux

## Member repositories
- hku-mars/Point-LIO (main) score 71

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:21.156683+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 2026-08-30T04:47:58.260131+00:00, confidence not recorded.
  - readme: https://github.com/hku-mars/Point-LIO (fetched 2026-08-28T04:04:21.156683+00:00, sha e12e2f213bd6)
- Data as of 2026-08-30T08:39:29.467469+00:00.