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

TL;DR

This paper introduces Wide-Area Geolocalization (WAG), a scalable method combining neural networks and particle filters to accurately localize a ground agent in city-scale regions using cross-view images without GPS.

Contribution

WAG's novel trinomial loss for Siamese networks and modified particle filter improve city-scale localization accuracy and efficiency, enabling GPS-denied environment navigation.

Findings

Achieves ~20 meters localization accuracy at city scale

Reduces position error by 64% compared to state-of-the-art

Significantly decreases storage and processing requirements

Abstract

Cross-view image geolocalization provides an estimate of an agent's global position by matching a local ground image to an overhead satellite image without the need for GPS. It is challenging to reliably match a ground image to the correct satellite image since the images have significant viewpoint differences. Existing works have demonstrated localization in constrained scenarios over small areas but have not demonstrated wider-scale localization. Our approach, called Wide-Area Geolocalization (WAG), combines a neural network with a particle filter to achieve global position estimates for agents moving in GPS-denied environments, scaling efficiently to city-scale regions. WAG introduces a trinomial loss function for a Siamese network to robustly match non-centered image pairs and thus enables the generation of a smaller satellite image database by coarsely discretizing the search area. A modified particle filter weighting scheme is also presented to improve localization accuracy and convergence. Taken together, WAG's network training and particle filter weighting approach achieves city-scale position estimation accuracies on the order of 20 meters, a 98% reduction compared to a baseline training and weighting approach. Applied to a smaller-scale testing area, WAG reduces the final position estimation error by 64% compared to a state-of-the-art baseline from the literature. WAG's search space discretization additionally significantly reduces storage and processing requirements.