# ankitdhall/lidar_camera_calibration

ROS package to find a rigid-body transformation between a LiDAR and a camera for "LiDAR-Camera Calibration using 3D-3D Point correspondences"

Repository: https://github.com/ankitdhall/lidar_camera_calibration
Canonical: https://ross.abutalabs.com/products/lidar_camera_calibration
Homepage: http://arxiv.org/abs/1705.09785
Language: C++
License: GPL-3.0
License Family: copyleft
Topics: camera, lidar, ros, calibration, velodyne, aruco-markers, point-cloud, lidar-camera-calibration, data-fusion, camera-calibration, hesai, point-clouds, ros-kinetic, ros-melodic, camera-frame, lidar-frame, 3d-points, stereo-cameras
Last push: 2025-10-16T18:17:31+00:00

## Health v2 (maintenance only)
Score: 44/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 47, release rhythm 8, longevity 100
- inputs: {"age_days": 3409, "days_push": 321, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1766, forks 475 (observed 2026-08-28T04:05:33.551195+00:00)

## What it is
A ROS package that computes the rigid-body transformation (rotation and translation) between a LiDAR and a camera using 3D-3D point correspondences, supporting monocular and stereo cameras and Hesai/Velodyne LiDARs. It is the open-source implementation of the paper 'LiDAR-Camera Calibration using 3D-3D Point correspondences' and includes tooling for fusing point clouds from multiple calibrated cameras.

## Use cases
- calibrate a velodyne lidar with a camera in ros
- find extrinsic transformation between lidar and camera frames
- fuse point clouds from two stereo cameras
- calibrate hesai lidar with monocular camera
- align lidar point cloud to camera image for autonomous driving
- estimate rotation and translation between lidar and camera using aruco markers

## When to choose
- you use ROS/ROS2 with Velodyne or Hesai LiDARs and need camera-LiDAR extrinsics
- you want a closed-form 3D-3D correspondence approach with aruco marker targets
- you need to fuse point clouds from multiple calibrated stereo cameras

## When to avoid
- you need targetless or online calibration without markers
- your stack is not ROS-based
- you need LiDAR-IMU or camera-IMU calibration instead

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, robotics, image-processing
- domain: robotics, autonomous-vehicles, computer-vision, simulation
- platform: cpp
- tags: lidar-camera-calibration, ros-package, aruco-markers, point-cloud, extrinsic-calibration, velodyne, hesai, sensor-fusion, stereo-camera, linux, ros

## Member repositories
- ankitdhall/lidar_camera_calibration (main) score 44

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:33.551195+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 2026-08-30T03:26:29.082731+00:00, confidence not recorded.
  - readme: https://github.com/ankitdhall/lidar_camera_calibration (fetched 2026-08-28T04:05:33.551195+00:00, sha 8a299493a32b)
  - homepage: http://arxiv.org/abs/1705.09785 (fetched 2026-08-29T11:04:53.225079+00:00, sha e6878f909885)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T11:04:53.234589+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T11:04:53.238626+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T11:04:53.240568+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T11:04:53.236538+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.