Ground Truth Generation for Quantitative Performance Evaluation of Localization Methods in Urban Areas

This paper presents an offline ground truth generation method using LIDAR(Light Detection and Ranging) scans and odometry. The generated ground truth allows quantitative evaluation of the performance of self-localization methods in urban areas where GNSS(Global Navigation Satellite System) cannot be trusted. The proposed method determines the vehicle pose (position and orientation) by aligning the LIDAR input with previously collected point cloud data. However, as alignment convergences are affected by the environment around the vehicle during each LIDAR scan, it can be erroneous. Incorrect estimates are removed and poses are interpolated by relying on odometry; which is locally accurate. A step by step optimization approach is adopted to yield the most accurate result. Experiments performed in a typical urban environment, with many buildings and surrounding obstacles, demonstrated the effectiveness of the proposed method.