Performance Evaluation of HD Map-Aided Localization for Land Vehicles in Underground Parking Using Integrated INS/GNSS and Low-Cost LiDAR

Abstract

There are many algorithms for localization and navigation, but achieving accurate positioning can be challenging, especially in heavy traffic or an unfriendly environment, and with low-cost, precise systems. Maintaining reliable positioning in these situations becomes difficult when GNSS signals are lost. To improve autonomous driving (AD), we need to advance research on vehicle localization techniques. This study presents a method that combines INS, GNSS, HD Maps, and Low-Cost LiDAR using the Extended Kalman Filter (EKF). We aim to enhance the performance of localization and navigation for AD by keeping positioning errors acceptable and supporting real-time decision-making in real-world scenarios. We tested this method in a challenging environment without GNSS signals, specifically an underground parking facility beneath the National Cheng Kung University (NCKU) library, where GNSS signals were blocked entirely. Our navigation algorithm achieved impressive 3D positioning differences of less than 0.30 m. The integration system showed significant performance improvements compared to the TC-INS/GNSS system. Compared to the traditional LC-INS/GNSS system, our approach improved by about 94.66%, 90.42%, and 93.93% in the ENU components. This accuracy is essential for precise “where-in-lane” positioning in AD applications.