Precise vehicle localization in dense urban environments

Abstract

In this contribution we introduce a framework for precise vehicle localization in dense urban environments which are characterized by high rates of dynamic and semi-static objects. The proposed localization method is specifically designed to handle inconsistencies between map material and sensor measurements. This is achieved by means of a robust map matching procedure based on the Fourier-Mellin transformation (FMT) for global vehicle pose estimation. Accurate and reliable relative localization is obtained from a LiDAR odometry. Consistency checks based on the cumulative sum (CUSUM) test are instrumented for rejection of inconsistent map matching results from the fusion procedure. Our key contributions are: i) Introduction and adaptation of a spectral map matching procedure based on the FMT for urban automated driving, ii) Presentation of a framework for efficient and robust localization in dense urban environments based on a novel LiDAR odometry, map matching, wheel odometry and GPS, iii) Proposal of a procedure for localization integrity monitoring which leads to significantly increased pose estimation accuracy. Evaluation results show the superior performance of the proposed approach compared to another state-of-the-art localization algorithm for a challenging urban dataset. All maps were recorded two years in advance of the evaluation test run. Furthermore, different LiDAR-based sensor setups were used for mapping and localization.