Determination of Optimum Coordinate Transformation Parameters for GNSS and LiDAR-Based Localization in Automated Vehicles

Abstract

Localization is one of the most critical components of automated vehicles (AVs). Although the positioning process in AVs is commonly carried out with global navigation satellite systems (GNSS), which offer a global solution, supportive or alternative location determination methods are needed due to the low accuracy of GNSS systems in urban areas, tunnels, etc. Laser Detection and Ranging -Simultaneous Localization and Mapping (LiDAR-SLAM) is an alternative localization method that provides the AV's local coordinates. This work aimed to verify the AV's localization by finding the accurate coordinate transformation parameters (CTPs) of trajectories acquired using the GNSS and LiDAR-SLAM technologies. We used the Conditional Adjustment with an Unknown Model (CAUM), Total Least Square (TLS), and Umeyama (UM) transformation methods to find the CTPs between the two sets of data. In order to address insoluble scenarios and enhance the efficiency of transformation, the voxel-based approach was developed, including the CAUM, TLS, and UM methods for each voxel. The results are discussed in terms of the performance comparison of the methods, the success of the voxel-based approach, and usability in real-time scenarious.