City-wide Street-to-Satellite Image Geolocalization of a Mobile Ground Agent
Lena M. Downes, Dong-Ki Kim, Ted J. Steiner, Jonathan P. How

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.…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Indoor and Outdoor Localization Technologies · Underwater Vehicles and Communication Systems
MethodsGreedy Policy Search · Siamese Network
