MCSGCalib: Multi-Constraint-Based Extrinsic Calibration of Solid-State LiDAR and GNSS/INS for Autonomous Vehicles

With the benefits of compact size and cost effectiveness, the solid-state LiDAR (SSL) has emerged as the preferred choice for the mass-produced vehicles with advanced driver assistance systems (ADAS). To ensure precise and dependable mapping for ADAS, it is essential to accurately fuse LiDAR and inertial measurements through precise extrinsic calibration. However, the existing extrinsic calibration methods primarily target the mechanical LiDAR and global navigation satellite system (GNSS)/inertial navigation system (INS), with limited research dedicated to the extrinsic calibration of SSL and GNSS/INS for the mass-produced vehicles. To achieve accurate calibration between the SSL and GNSS/INS, we develop a targetless extrinsic calibration method based on multiple constraints, called MCSGCalib. Specifically, we first propose a high-level geometric feature extraction approach based on the adaptive voxel to efficiently obtain the high-level geometric features for the SSL. Based on the extracted geometric features and ego-motion information, we construct multiple derived-friendly constraints, and fuse the motion constraints and geometric constraints into a unified manifold optimization framework in order to accurately estimate extrinsic parameters for the mass-produced vehicles, even in the presence of degenerate motions. To validate the performance of MCSGCalib, we conduct comprehensive experiments using the data collected from our mass-produced vehicles equipped with RS-LiDAR-M1, a recently released SSL extensively deployed in the mass-produced vehicles. The experimental results demonstrate the accuracy and robustness of the MCSGCalib in calibrating the extrinsic parameters between the SSL and GNSS/INS, showcasing its potential in enhancing the reliability and quality of the SSL and GNSS/INS fusion for ADAS.