Extrinsic Calibration of a 3D-LIDAR and a Camera
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Mar 2020.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 1; 2024: 1.
5 papers.
- 2021SemCal: Semantic LiDAR-Camera Calibration using Neural MutualInformation Estimatorcited by 7
- 2021Calibrating LiDAR and Camera using Semantic Mutual informationcited by 1
- 2023RobustCalib: Robust Lidar-Camera Extrinsic Calibration with Consistency Learningcited by 1
- 2024Cooperative Visual-LiDAR Extrinsic Calibration Technology for Intersection Vehicle-Infrastructure: A reviewcited by 1
- 2021Optimal Target Shape for LiDAR Pose Estimationcited by 0
- 2024Cooperative Visual-LiDAR Extrinsic Calibration Technology for Intersection Vehicle-Infrastructure: A review16 May 2024
- 2023RobustCalib: Robust Lidar-Camera Extrinsic Calibration with Consistency Learning2 Dec 2023
- 2021SemCal: Semantic LiDAR-Camera Calibration using Neural MutualInformation Estimator21 Sep 2021
- 2021Optimal Target Shape for LiDAR Pose Estimation2 Sep 2021
- 2021Calibrating LiDAR and Camera using Semantic Mutual information24 Apr 2021