Extrinsic Calibration of Three-Dimensional LiDAR Using Sphere Targets and Differential-Corrected Global Positioning System

Abstract

Abstract. This article presents an extrinsic calibration approach for locating the exact pose of a 3D light detection and ranging (LiDAR) sensor suitable for autonomous or mapping vehicles using sphere targets and multiple differential-corrected Global Positioning System (DGPS) antennas. The method employs constrained random sample consensus (RANSAC) for robust sphere detection, least-squares fitting for precise center estimation, and an iterative point-to-point alignment using singular value decomposition (SVD) for computing the pose transformations. Experimental validation using road lane markers demonstrates a mean positional accuracy of 3.6 cm, with a standard deviation of 1.3 cm and a maximum error of 7.1 cm. The methodology can achieve calibration either with single-scan data or by aggregating data across scans. These results confirm the method’s effectiveness in improving spatial data accuracy for autonomous navigation, HD map generation, and multisensor fusion applications.