UAV Visual Localization Method Based on Token-Level Local Matching Reranking and Neighborhood-Consistent Position Fusion

Abstract

UAV visual localization aims to utilize real-time ground observation imagery captured by drone platforms to retrieve the most relevant images from a large-scale satellite remote sensing image database and thereby estimate their corresponding geographic coordinates. It is a critical task in autonomous navigation of unmanned systems, emergency reconnaissance, and low-altitude remote sensing applications. Existing UAV visual localization methods typically rely on global feature similarity to rank candidate satellite tiles and directly adopt the center of the Top-1 tile as the localization result, leading to unstable rankings and coordinate estimation errors in continuous area localization tasks. To address these issues, this paper proposes a token-level local matching reranking method and a neighborhood-consistent position fusion method for UAV visual localization. First, a global search efficiently retrieves a Top-K candidate set from a large-scale reference database. Second, a token-level local matching reranking module is introduced, which utilizes local token interactions, neighborhood geometric priors, and candidate relationship modeling to perform fine-grained reranking and score calibration of high-confidence candidates, thereby enhancing the reliability of the top candidates’ rankings. Finally, a neighborhood-consistent position fusion strategy is proposed, which adaptively fuses and predicts position coordinates by jointly utilizing the spatial distribution and confidence relationships of multiple candidate satellite tiles to mitigate the discretization errors caused by center-based localization using a single tile. Experimental results on the GTA-UAV and UAV-VisLoc datasets demonstrate that the proposed method effectively improves candidate ranking quality and reduces meter-level localization errors under same-area settings. Compared with the Global Retrieval baseline, over five independent runs under the GTA-UAV same-area setting, the proposed method achieves an average Recall@1 (R@1) gain of 2.89 percentage points and reduces the average Dis@1 localization error by 60.95 m. In a single-seed evaluation under the UAV-VisLoc same-area setting, it also improves R@1 by 3.03 percentage points and reduces Dis@1 localization error by 39.53 m.