Visual Geo-Localization Based on Spatial Structure Feature Enhancement and Adaptive Scene Alignment

The task of visual geo-localization based on street-view images estimates the geographical location of a query image by recognizing the nearest reference image in a geo-tagged database. This task holds considerable practical significance in domains such as autonomous driving and outdoor navigation. Current approaches typically use perspective street-view images as reference images. However, the lack of scene content resulting from the restricted field of view (FOV) in such images is the main cause of inaccuracies in matching and localizing the query and reference images with the same global positioning system (GPS) labels. To address this issue, we propose a perspective-to-panoramic image visual geo-localization framework. This framework employs 360° panoramic images as references, thereby eliminating the issue of scene content mismatch due to the restricted FOV. Moreover, we propose the structural feature enhancement (SFE) module and integrate it into LskNet to enhance the ability of the feature extraction network to capture and extract long-term stable structural features. Furthermore, we propose the adaptive scene alignment (ASA) strategy to address the issue of data capacity and information content asymmetry between perspective and panoramic images, thereby facilitating initial scene alignment. In addition, a lightweight feature aggregation module, MixVPR, which considers spatial structure relationships, is introduced to aggregate the scene-aligned region features into robust global feature descriptors for matching and localization. Experimental results demonstrate that the proposed model outperforms current state-of-the-art methods and achieves R@1 scores of 72.5% on the Pitts250k-P2E dataset and 58.4% on the YQ360 dataset, indicating the efficacy of this approach in practical visual geo-localization applications.