Robust Global Localization for Urban Autonomous Vehicles via 3D Geometric-Enhanced Visual Place Recognition

Accurate and robust long-term global localization is a critical challenge for autonomous vehicles operating in complex urban transportation systems, where GPS signals are often unreliable and visual/inertial odometry suffers from inevitable error accumulation. Visual Place Recognition (VPR) offers a crucial solution by detecting loop closures to mitigate trajectory drift, but its performance severely degrades under complex urban traffic scenarios, such as drastic changes in viewpoint, illumination, and weather. To address these limitations, we propose the 3D Geometric feature Enhanced VPR (GE-VPR), a novel framework that improves the robustness of vehicular global localization. As a purely vision-based system, GE-VPR reconstruct 3D point clouds through dense simultaneous localization and mapping. A 3D geometric feature extraction network is designed to obtain the stable structural features of the point clouds, and a 2D-3D hybrid network is then developed to further augment these 3D features with 2D semantics. Additionally, a descriptor refinement strategy is proposed to fine-tune the raw 2D descriptors by aggregating the most relevant hybrid structural features, thus effectively fusing rich 2D appearance, color, and semantic information with stable 3D geometry. Extensive experiments on challenging urban autonomous driving datasets demonstrate that GE-VPR significantly improves vehicle localization accuracy and robustness. The overall recognition recall is increased by more than 5%, and the positioning accuracy is significantly improved in practical scenarios, demonstrating its potential as an effective solution to improve the safety and reliability of vehicular localization in autonomous navigation systems.