# PiLiDAR/PiLiDAR

Repository: https://github.com/PiLiDAR/PiLiDAR
Canonical: https://ross.abutalabs.com/products/pilidar
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-05-08T19:53:06+00:00

## Health v2 (maintenance only)
Score: 59/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 81, release rhythm 35, longevity 51
- inputs: {"age_days": 725, "days_push": 117, "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 1954, forks 93 (observed 2026-08-28T04:05:58.910926+00:00)

## What it is
PiLiDAR is a DIY 360° 3D panorama scanner built on Raspberry Pi that combines a low-cost LDRobot LiDAR (LD06/LD19/STL27L) with a Pi HQ camera to produce colored 3D point clouds. It stitches fisheye panoramas with Hugin, assembles 3D scenes from 2D LiDAR scans, and exports to PCD, PLY, or e57 formats.

## Use cases
- build a DIY 3D lidar scanner with raspberry pi
- capture colored 3d point clouds of rooms
- stitch 360 panorama from fisheye photos
- export lidar scans to PLY or e57
- visualize 2d lidar data live
- register and align multiple 3d scans with ICP

## When to choose
- you want a low-cost (~$200-280) DIY 3D scanning rig
- you use Raspberry Pi with LDRobot LD06/LD19/STL27L sensors
- you need vertex-colored point clouds from panorama stitching

## When to avoid
- you need production-grade surveying accuracy
- you want plug-and-play commercial scanner software
- you need fast Poisson meshing on-device (recommended on PC)

## Facets
- artifact type: application
- maturity: experimental
- function: image-processing, computer-vision, data-visualization, serialization, developer-tools
- domain: robotics, hardware, graphics, simulation
- platform: python, embedded
- tags: lidar, 3d-scanning, point-cloud, panorama, raspberry-pi, diy-hardware, open3d, photogrammetry, linux

## Member repositories
- PiLiDAR/PiLiDAR (main) score 59

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:58.910926+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 2026-08-30T03:06:31.927625+00:00, confidence not recorded.
  - readme: https://github.com/PiLiDAR/PiLiDAR (fetched 2026-08-28T04:05:58.910926+00:00, sha 5efc940f06b6)
- Data as of 2026-08-30T08:39:29.467469+00:00.