Wide-Area Geolocalization with a Limited Field of View Camera

• URL: http://arxiv.org/abs/2209.11854v2
• Date: Thu, 18 May 2023 14:41:01 GMT
• Title: Wide-Area Geolocalization with a Limited Field of View Camera
• Authors: Lena M. Downes, Ted J. Steiner, Rebecca L. Russell, and Jonathan P. How
• Abstract summary: 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. 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.
• Score: 33.34809839268686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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