Vehicle Localization Using Lidar and Voxel Maps: Benchmarking Point Cloud Registration Methods

Abstract

ABSTRACT Accurate and reliable localization is a key requirement for modern automated vehicles. In densely built‐up urban environments such as city canyons, where satellite‐based positioning methods are affected by signal interference or blockage, vehicle‐mounted lidar can harvest the rich geometric features found on human‐made structures and increase the availability of a localization solution. Lidar‐based odometry and simultaneous localization and mapping (SLAM) approaches do, however, require loop closing or they suffer from accumulated error. A promising approach to address such problems is to employ voxelized High Definition (HD) maps, which provide a compact, effective, and low‐maintenance solution. Despite the advantages of voxelized HD maps, their potential for localization through registration with lidar scans has not been explored. In this work, we evaluate the performance of various registration methods for lidar localization with voxelized HD maps. The results show that the registration methods that rely on matching local surface geometry fail when applied to voxelized point clouds. However, methods that match individual points or superpoints, namely GeoTransformer and two variants of the iterative closest point (ICP), achieve an acceptable performance with voxelized data and potentially support lane‐level accuracy when provided with initialization within 10 m from the ground truth location.