Wide-Area Geolocalization with a Limited Field of View Camera

TL;DR

ReWAG is a novel cross-view geolocalization method that enables GPS-denied localization using a standard 90-degree FOV camera, matching panoramic camera accuracy and vastly outperforming baseline vision transformer approaches.

Contribution

This work extends the WAG framework to non-panoramic cameras by developing pose-aware embeddings and integrating particle pose into the neural network, broadening applicability for robotic platforms.

Findings

ReWAG achieves city-scale localization accuracy with a standard camera.

ReWAG improves localization accuracy by 100x over baseline ViT methods.

ReWAG successfully localizes over several dozen kilometers in a GPS-denied environment.

Abstract

Cross-view geolocalization, a supplement or replacement for GPS, localizes an agent within a search area by matching images taken from a ground-view camera to overhead images taken from satellites or aircraft. Although the viewpoint disparity between ground and overhead images makes cross-view geolocalization challenging, significant progress has been made assuming that the ground agent has access to a panoramic camera. For example, our prior work (WAG) introduced changes in search area discretization, training loss, and particle filter weighting that enabled city-scale panoramic cross-view geolocalization. However, panoramic cameras are not widely used in existing robotic platforms due to their complexity and cost. Non-panoramic cross-view geolocalization is more applicable for robotics, but is also more challenging. This paper presents Restricted FOV Wide-Area Geolocalization (ReWAG), a cross-view geolocalization approach that generalizes WAG for use with standard, non-panoramic ground cameras by creating pose-aware embeddings and providing a strategy to incorporate particle pose into the Siamese network. ReWAG is a neural network and particle filter system that is able to globally localize a mobile agent in a GPS-denied environment with only odometry and a 90 degree FOV camera, achieving similar localization accuracy as what WAG achieved with a panoramic camera and improving localization accuracy by a factor of 100 compared to a baseline vision transformer (ViT) approach. A video highlight that demonstrates ReWAG's convergence on a test path of several dozen kilometers is available at https://youtu.be/U_OBQrt8qCE.