Laser reflectance feature assisted accurate extrinsic calibration for non-repetitive scanning LiDAR and camera systems

Abstract

Non-repetitive scanning Light Detection And Ranging(LiDAR)-Camera systems are commonly used in autonomous navigation industries, benefiting from their low-cost and high-perception characteristics. However, due to the irregular scanning pattern of LiDAR, feature extraction on point cloud encounters the problem of non-uniformity distribution of density and reflectance intensity, accurate extrinsic calibration remains a challenging task. To solve this problem, this paper presented an open-source calibration method using only a printed chessboard. We designed a two-stage coarse-to-fine pipeline for 3D corner extraction. Firstly, a Gaussian Mixture Model(GMM)-based intensity cluster approach is proposed to adaptively identify point segments in different color blocks of the chessboard. Secondly, a novel Iterative Lowest-cost Pose(ILP) algorithm is designed to fit the chessboard grid and refine the 3D corner iteratively. This scheme is unique for turning the corner feature extraction problem into a grid align problem. After the corresponding 3D-2D points are solved, by applying the PnP(Perspective-n-Point) method, along with nonlinear-optimization refinement, the extrinsic parameters are obtained. Extensive simulation and real-world experimental results show that our method achieved subpixel-level precision in terms of reprojection error. The comparison demonstrated that the effectiveness and accuracy of the proposed method outperformed existing methods.