  Cross-view matching refers to the problem of finding the closest match for a
given query ground view image to one from a database of aerial images. If the
aerial images are geotagged, then the closest matching aerial image can be used
to localize the query ground view image. Due to the recent success of deep
learning 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, we
evaluate cross-view matching for the task of localizing a ground vehicle over a
longer trajectory. We treat these cross-view matches as sensor measurements
that are fused using a particle filter. We evaluate the performance of this
method using a city-wide dataset collected in a photorealistic simulation by
varying four parameters: height of aerial images, the pitch of the aerial
camera mount, FOV of the ground camera, and the methodology of fusing
cross-view measurements in the particle filter. We also report the results
obtained using our pipeline on a real-world dataset collected using Google
Street View and satellite view APIs.
