GRiM-Net: A Two-Stage Cross-View Visual Localization Framework for UAVs

Autonomous flight of unmanned aerial vehicles (UAVs) in Global Navigation Satellite System (GNSS)-denied environments critically depends on accurate and robust visual localization. To tackle the challenges of cross-view domain discrepancies and real-time high-precision matching, we propose GRiM-Net, a two-stage joint optimization visual localization network. First, a global retrieval module aggregates features and selects the most similar satellite map candidate patches from a pre-built index, efficiently narrowing the search from the global map to a local region. Next, a fine matching module performs pixel-level keypoint detection and description on the query image and candidate patches. Bidirectional matching and weighted homography estimation are then used to map the UAV image center to satellite coordinates, yielding precise geographic positions. Both modules share a backbone with domain-adaptive batch normalization, and joint optimization of global retrieval triplet loss with fine matching keypoint, descriptor, and homography reprojection losses enables synergistic enhancement of feature representations. Ablation and comparison experiments conducted on public urban cross-view benchmarks demonstrate that GRiM-Net can achieve efficient and robust geographic coordinate regression for UAVs, providing a practical localization component for broader navigation systems.