Second-Order Robust Iterative Pose Optimization for Fine-Grained Cross-View Localization

Fine-grained cross-view localization seeks to estimate precise camera poses by matching ground images with GPS-tagged aerial imagery. Existing methods typically employ first-order iterative optimization to progressively update the camera pose based on cross-view feature correspondences. However, they rely on local features and neglect global and complementary contextual information, making them prone to local optima and slow convergence under large initial errors or strong disturbances. To overc