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Cross-view matching refers to the problem of finding the closest match for agiven query ground view image to one from a database of aerial images. If theaerial images are geotagged, then the closest matching aerial image can be usedto localize the query ground view image. Due to the recent success of deeplearning methods, several cross-view matching techniques have been proposed.These approaches perform well for the matching of isolated query images.However, their evaluation over a trajectory is limited. In this paper, weevaluate cross-view matching for the task of localizing a ground vehicle over alonger trajectory. We treat these cross-view matches as sensor measurementsthat are fused using a particle filter. We evaluate the performance of thismethod using a city-wide dataset collected in a photorealistic simulation byvarying four parameters: height of aerial images, the pitch of the aerialcamera mount, FOV of the ground camera, and the methodology of fusingcross-view measurements in the particle filter. We also report the resultsobtained using our pipeline on a real-world dataset collected using GoogleStreet View and satellite view APIs.

Evaluation of Cross-View Matching to Improve Ground Vehicle Localization with Aerial Perception