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City-wide Street-to-Satellite Image Geolocalization of a Mobile Ground Agent by Downes, Lena M.; Kim, Dong-Ki; Steiner, Ted J.; How, Jonathan P. is a scholarly article available to read on EtoBox.

What is City-wide Street-to-Satellite Image Geolocalization of a Mobile Ground Agent about?

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 ba

Author

Downes, Lena M.; Kim, Dong-Ki; Steiner, Ted J.; How, Jonathan P.

Published

2022

Language

EN