Wide-Area Geolocalization with a Limited Field of View Camera in Challenging Urban Environments

TL;DR

This paper introduces ReWAG, a novel cross-view geolocalization method using limited FOV cameras and neural networks, significantly improving localization accuracy in urban environments without panoramic cameras.

Contribution

ReWAG combines pose-aware embeddings with a particle filter to enable accurate geolocalization using non-panoramic cameras, and ReWAG* enhances generalization to unseen environments.

Findings

ReWAG improves localization accuracy by 100x over baseline.

ReWAG* successfully localizes in new environments with unseen FOVs.

The approach works effectively with 72-degree FOV cameras in urban settings.

Abstract

Cross-view geolocalization, a supplement or replacement for GPS, localizes an agent within a search area by matching ground-view images to overhead images. Significant progress has been made assuming a panoramic ground camera. Panoramic cameras' high complexity and cost make non-panoramic cameras more widely applicable, but also more challenging since they yield less scene overlap between ground and overhead images. This paper presents Restricted FOV Wide-Area Geolocalization (ReWAG), a cross-view geolocalization approach that combines a neural network and particle filter to globally localize a mobile agent with only odometry and a non-panoramic camera. ReWAG creates pose-aware embeddings and provides a strategy to incorporate particle pose into the Siamese network, improving localization accuracy by a factor of 100 compared to a vision transformer baseline. This extended work also presents ReWAG*, which improves upon ReWAG's generalization ability in previously unseen environments. ReWAG* repeatedly converges accurately on a dataset of images we have collected in Boston with a 72 degree field of view (FOV) camera, a location and FOV that ReWAG* was not trained on.