  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.
