IFMIR-VR: Visual Relocalization for Autonomous Vehicles Using Integrated Feature Matching and Image Retrieval
Gang Li, Xiaoman Xu, Jian Yu, Hao Luo · Applied Sciences · 2025
Relocalization technology is an important part of autonomous vehicle navigation. It allows the vehicle to find its position on the map after a reboot. This paper presents a relocalization algorithm framework that uses image retrieval techniques. An integrated matching algorithm is applied during the feature matching process. This improves the accuracy of the vehicle’s relocalization. We use image retrieval to select the most relevant image from the map database. The integrated matching algorithm then finds precise feature correspondences. Using these correspondences and depth information, we calculate the vehicle’s global pose with the Perspective-n-Point (PnP) and Levenberg–Marquardt (L-M) algorithms. This process helps the vehicle determine its position on the map. Experimental results on public datasets show that the proposed framework outperforms existing methods like LightGlue and LoFTR in terms of matching accuracy.