Robust extrinsic parameter calibration of 3D LIDAR using lie algebras

In the field of autonomous driving, multi-beam light detection and ranging (3D LIDAR) system and global navigation satellite system/integrated inertial navigation system (GNSS/INS) are widely used in high-definition map construction, localization and obstacle detection. As 3D LIDAR system and INS have their own coordinate systems, the calibration of the two mentioned systems is required. In this paper, a novel algorithm for calibrating the coordinate system of 3D LIDAR and INS is proposed, which consists of three parts. The first procedure is to project two point clouds to the world coordinate system based on the initial transform matrix between 3D LIDAR and INS with the real-time data from INS. Then optimal point-to-point correspondences can be found between two frames of point cloud data through registration method. Finally, the loss function is constructed with the sum of the Euclidean distances of the corresponding points and optimized by using perturbation model of Lie algebras, so as to obtain the optimal transform matrix. With different given initial calibration parameters, test results of both simulation and real experiments validate the proposed algorithm and quantify its accuracy and robustness.