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Cross-view image geolocalization provides an estimate of an agent's globalposition by matching a local ground image to an overhead satellite imagewithout the need for GPS. It is challenging to reliably match a ground image tothe correct satellite image since the images have significant viewpointdifferences. Existing works have demonstrated localization in constrainedscenarios over small areas but have not demonstrated wider-scale localization.Our approach, called Wide-Area Geolocalization (WAG), combines a neural networkwith a particle filter to achieve global position estimates for agents movingin GPS-denied environments, scaling efficiently to city-scale regions. WAGintroduces a trinomial loss function for a Siamese network to robustly matchnon-centered image pairs and thus enables the generation of a smaller satelliteimage database by coarsely discretizing the search area. A modified particlefilter weighting scheme is also presented to improve localization accuracy andconvergence. Taken together, WAG's network training and particle filterweighting approach achieves city-scale position estimation accuracies on theorder of 20 meters, a 98% reduction compared to a baseline training andweighting approach. Applied to a smaller-scale testing area, WAG reduces thefinal position estimation error by 64% compared to a state-of-the-art baselinefrom the literature. WAG's search space discretization additionallysignificantly reduces storage and processing requirements.

City-wide Street-to-Satellite Image Geolocalization of a Mobile Ground Agent