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Open AccessArticle

Accurate Calibration of Multi-LiDAR-Multi-Camera Systems

1
Geometric Computer Vision Group, Machine Perception Laboratory, MTA SZTAKI, Kende st. 17, 1111 Budapest, Hungary
2
Department of Algorithms and Their Applications, Eötvös Loránd University, Pázmány Péter stny. 1/C., 1117 Budapest, Hungary
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(7), 2139; https://doi.org/10.3390/s18072139
Received: 26 May 2018 / Revised: 25 June 2018 / Accepted: 29 June 2018 / Published: 3 July 2018
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
As autonomous driving attracts more and more attention these days, the algorithms and sensors used for machine perception become popular in research, as well. This paper investigates the extrinsic calibration of two frequently-applied sensors: the camera and Light Detection and Ranging (LiDAR). The calibration can be done with the help of ordinary boxes. It contains an iterative refinement step, which is proven to converge to the box in the LiDAR point cloud, and can be used for system calibration containing multiple LiDARs and cameras. For that purpose, a bundle adjustment-like minimization is also presented. The accuracy of the method is evaluated on both synthetic and real-world data, outperforming the state-of-the-art techniques. The method is general in the sense that it is both LiDAR and camera-type independent, and only the intrinsic camera parameters have to be known. Finally, a method for determining the 2D bounding box of the car chassis from LiDAR point clouds is also presented in order to determine the car body border with respect to the calibrated sensors. View Full-Text
Keywords: LiDAR; camera; LiDAR camera system; machine perception; extrinsic calibration; autonomous driving LiDAR; camera; LiDAR camera system; machine perception; extrinsic calibration; autonomous driving
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MDPI and ACS Style

Pusztai, Z.; Eichhardt, I.; Hajder, L. Accurate Calibration of Multi-LiDAR-Multi-Camera Systems. Sensors 2018, 18, 2139.

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