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This paper presents a novel method for fully automatic and convenientextrinsic calibration of a 3D LiDAR and a panoramic camera with a normallyprinted chessboard. The proposed method is based on the 3D corner estimation ofthe chessboard from the sparse point cloud generated by one frame scan of theLiDAR. To estimate the corners, we formulate a full-scale model of thechessboard and fit it to the segmented 3D points of the chessboard. The modelis fitted by optimizing the cost function under constraints of correlationbetween the reflectance intensity of laser and the color of the chessboard'spatterns. Powell's method is introduced for resolving the discontinuity problemin optimization. The corners of the fitted model are considered as the 3Dcorners of the chessboard. Once the corners of the chessboard in the 3D pointcloud are estimated, the extrinsic calibration of the two sensors is convertedto a 3D-2D matching problem. The corresponding 3D-2D points are used tocalculate the absolute pose of the two sensors with Unified Perspective-n-Point(UPnP). Further, the calculated parameters are regarded as initial values andare refined using the Levenberg-Marquardt method. The performance of theproposed corner detection method from the 3D point cloud is evaluated usingsimulations. The results of experiments, conducted on a Velodyne HDL-32e LiDARand a Ladybug3 camera under the proposed re-projection error metric,qualitatively and quantitatively demonstrate the accuracy and stability of thefinal extrinsic calibration parameters.

Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard