Cross-view matching refers to the problem of finding the closest match to 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. Recently, due to the success of deep learning methods, a number of cross-view matching techniques have been proposed. These techniques perform well for the matching of isolated query images. In this paper, we evaluate cross-view matching for the task of localizing a ground vehicle over a longer trajectory. We use the cross-view matching module as a sensor measurement fused with a particle filter. We evaluate the performance of this method using a city-wide dataset collected in photorealistic simulation using five parameters: height of aerial images, the pitch of the aerial camera mount, field-of-view of ground camera, measurement model and resampling strategy for the particles in the particle filter.
We are motivated by environmental monitoring tasks where finding the global maxima (i.e., hotspot) of a spatially varying field is crucial. We investigate the problem of identifying the hotspot for fields that can be sensed using an Unmanned Aerial Vehicle (UAV) equipped with a downward-facing camera. The UAV has a limited time budget which it must use for learning the unknown field and identifying the hotspot. Our first contribution is to show how this problem can be formulated as a novel variant of the Gaussian Process (GP) multi-armed bandit problem. The novelty is two-fold: (i) unlike standard multi-armed bandit settings, the arms ; and (ii) unlike standard GP regression, the measurements in our problem are image (i.e., vector measurements) whose quality depends on the altitude at which the UAV flies. We present a strategy for finding the sequence of UAV sensing locations and empirically compare it with a number of baselines. We also present experimental results using images gathered onboard a UAV.