# Extrinsic Calibration of a 3D-LIDAR and a Camera

arXiv: https://arxiv.org/abs/2003.01213 · HTML: https://mlsanity.com/paper/2003.01213/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [SemCal: Semantic LiDAR-Camera Calibration using Neural MutualInformation Estimator](https://mlsanity.com/paper/2109.10270/) (2021)
- [Calibrating LiDAR and Camera using Semantic Mutual information](https://mlsanity.com/paper/2104.12023/) (2021)
- [RobustCalib: Robust Lidar-Camera Extrinsic Calibration with Consistency Learning](https://mlsanity.com/paper/2312.01085/) (2023)
- [Cooperative Visual-LiDAR Extrinsic Calibration Technology for Intersection Vehicle-Infrastructure: A review](https://mlsanity.com/paper/2405.10132/) (2024)
- [Optimal Target Shape for LiDAR Pose Estimation](https://mlsanity.com/paper/2109.01181/) (2021)

## Abstract (arXiv)

This work presents an extrinsic parameter estimation algorithm between a 3D LIDAR and a Projective Camera using a marker-less planar target, by exploiting Planar Surface Point to Plane and Planar Edge Point to back-projected Plane geometric constraints. The proposed method uses the data collected by placing the planar board at different poses in the common field of view of the LIDAR and the Camera. The steps include, detection of the target and the edges of the target in LIDAR and Camera frames, matching the detected planes and lines across both the sensing modalities and finally solving a cost function formed by the aforementioned geometric constraints that link the features detected in both the LIDAR and the Camera using non-linear least squares. We have extensively validated our algorithm using two Basler Cameras, Velodyne VLP-32 and Ouster OS1 LIDARs.