Creation and Verification of High-Definition Point Cloud Maps for Autonomous Vehicle Navigation

High-definition (HD) maps have recently become a key piece of technology in autonomous driving. Over the past few years, various methods and sensors, such as those based on inertial navigation system (INS), global navigation satellite system (GNSS), cameras, and light detection and ranging (LiDAR), have been used to develop HD maps. In this study, we developed novel techniques for enhancing the creation and verification of HD point cloud maps. First, a tightly coupled (TC) INS/GNSS-assisted 3-D normal distribution transform (NDT)-LiDAR mapping system has been developed. Utilizing an integrated INS/GNSS, the system provides a reliable initial pose, thereby mitigating the issue of divergence in NDT scan matching, particularly when the vehicle operates at high speeds in challenging LiDAR environments. This approach enhances both navigation accuracy and the precision of the point cloud map. Second, alternative ground control points (GCPs) have been established as substitutes for conventional techniques, addressing freeway regulations and managing safety concerns. Third, to ensure the desired accuracy for “where-in-lane” positioning in autonomous vehicle applications, the created point cloud map was validated against the criteria outlined by standardized procedures. Overall, our preliminary results indicate that our HD point cloud map meets the positioning accuracy criteria outlined by the Taiwan Association of Information and Communication Standards. Our point density results also indicate that our generated point cloud map can achieve a high degree of accuracy in in-lane positioning for autonomous vehicle navigation.