关键词: GNSS拒止, 无人机, 视觉地理定位, 由粗到精, 密集特征匹配, 物理先验

Abstract: In complex environments where satellite positioning signals are unavailable, UAV visual geo-localization is prone to matching failures due to sparse scene textures. Meanwhile, the geographical continuity and local similarity inherent in satellite imagery can introduce ambiguity and localization drift when relying solely on the Top-1 result from global retrieval. To address these challenges, this study develops a coarse-to-fine visual localization method incorporating physical prior constraints. First, in the coarse retrieval stage, global features are used to retrieve the Top-K candidate regions from a satellite tile database, thereby covering all potential locations. Subsequently, in the fine matching stage, an adaptive hypothesis verification procedure is designed, in which a Transformer-based model with a global receptive field is employed for dense feature matching. This enables robust correspondences to be established between UAV and satellite views, thereby alleviating matching degradation caused by insufficient texture. Furthermore, a physical-prior-guided outlier rejection algorithm is proposed. By jointly enforcing the convexity constraint of the homography transformation and the projection-boundary consistency check, geometrically infeasible mismatches are strictly eliminated. Candidate tiles are then re-ranked according to inlier quality to determine the optimal solution. Experimental results on the UAV-VisLoc dataset demonstrate that, compared with the baseline method that directly outputs the Top-1 candidate, the proposed method significantly improves localization accuracy under small- and medium-error thresholds. Specifically, the localization accuracies at error thresholds of 10 m, 30 m, and 50 m are increased by 11.44, 11.33, and 10.22 percentage points, respectively. Under the setting of five candidate regions, the end-to-end average runtime is 0.706 s. Overall, the proposed method effectively mitigates the degradation caused by sparse features while achieving meter-level localization accuracy with favorable computational efficiency.

Key words: GNSS-denied, Unmanned Aerial Vehicle (UAV), Visual Geo-Localization, Coarse-to-Fine, Dense Feature Matching, Physics-Informed Priors