  Our work is 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 can use for learning the
unknown field and identifying the hotspot. Our contribution is to show how this
problem can be formulated as a novel multi-fidelity variant of the Gaussian
Process (GP) multi-armed bandit problem. The novelty is two-fold: (i) unlike
standard multi-armed bandit settings, the rewards of the arms are correlated
with each other; and (ii) unlike standard GP regression, the measurements in
our problem are images (i.e., vector measurements) whose quality depends on the
altitude of the UAV. We present a strategy for finding the sequence of UAV
sensing locations and empirically compare it with several baselines.
Experimental results using images gathered onboard a UAV are also presented and
the scalability of the proposed methodology is assessed in a large-scale
simulated environment in Gazebo.
