# cuitaixiang/LOAM_NOTED

loam code noted in Chinese（loam中文注解版）

Repository: https://github.com/cuitaixiang/LOAM_NOTED
Canonical: https://ross.abutalabs.com/products/loam_noted
Language: C++
License Family: other
Topics: loam, loam-velodyne, slam, lidar, odometry, mapping, velodyne, pointcloud, notes, pcl
Last push: 2019-10-09T01:53:45+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": 3069, "days_push": 2521, "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 1088, forks 440 (observed 2026-08-28T04:03:32.514615+00:00)

## What it is
A Chinese-annotated version of the LOAM (LiDAR Odometry and Mapping) source code, accompanied by related papers. It serves as a detailed study resource for understanding real-time LiDAR SLAM implementation.

## Use cases
- learn how LOAM lidar odometry and mapping works
- study annotated SLAM source code line by line
- understand point cloud registration algorithms
- prepare to implement or modify LOAM for a robotics project
- read LOAM-related papers alongside the code

## When to choose
- you read Chinese and want deeply annotated LOAM code
- you are learning LiDAR SLAM from a real implementation
- you want the original LOAM papers bundled with the code

## When to avoid
- you need a maintained, production-ready SLAM library
- you cannot read Chinese and dislike machine-translated comments
- you need a license permitting redistribution or commercial use

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: robotics, computer-vision, developer-tools
- domain: robotics, autonomous-vehicles, tutorials
- platform: cpp, cross-platform
- tags: slam, lidar, loam, odometry, pointcloud, annotated-code, chinese, pcl, velodyne, algorithms, linux

## Member repositories
- cuitaixiang/LOAM_NOTED (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:32.514615+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 2026-08-30T06:49:21.334130+00:00, confidence not recorded.
  - readme: https://github.com/cuitaixiang/LOAM_NOTED (fetched 2026-08-28T04:03:32.514615+00:00, sha 5a29f85a7902)
- Data as of 2026-08-30T08:39:29.467469+00:00.