An INS-Centric Locator for Autonomous Vehicles Aided by GNSS, Monocular Visual-Inertial Odometry, and HD Vector Maps

Abstract. Reliable lane-level localization remains difficult for autonomous vehicles (AVs) when Global Navigation Satellite System (GNSS) observations are degraded by blockage, multipath, and non-line-of-sight reception in urban environments. This paper presents PointLoc, an Inertial Navigation System (INS)-centric locator aided by GNSS, monocular visual-inertial odometry (VIO), and High-Definition (HD) Vector Maps. The proposed method is formulated as an INS-centric error-state extended Kalman filter (EKF), in which the INS provides persistent state propagation, while GNSS, VIO, and map matching are incorporated as aiding updates according to their availability and reliability. This design preserves a unified position, velocity, and attitude solution and enables graceful degradation when some aiding sources become unavailable. The method is validated through real-vehicle experiments in Taichung Shuinan and Tainan Shalun under mixed GNSS conditions. The results show that PointLoc achieves the best overall full-route performance in Taichung Shuinan and remains broadly comparable to GNSS/INS/VIO, while still outperforming GNSS/INS, in Tainan Shalun. In the mapped GNSS-denied segment of Taichung Shuinan, PointLoc effectively suppresses vertical drift and substantially improves three-dimensional positioning. The mapped-road analysis further shows that the INS-centric design avoids the planar instability observed in a vision-centric benchmark and provides a more continuous localization solution.