  We consider a scenario in which an autonomous vehicle equipped with a
downward facing camera operates in a 3D environment and is tasked with
searching for an unknown number of stationary targets on the 2D floor of the
environment. The key challenge is to minimize the search time while ensuring a
high detection accuracy. We model the sensing field using a multi-fidelity
Gaussian process that systematically describes the sensing information
available at different altitudes from the floor. Based on the sensing model, we
design a novel algorithm called Expedited Multi-Target Search (EMTS) that (i)
addresses the coverage-accuracy trade-off: sampling at locations farther from
the floor provides wider field of view but less accurate measurements, (ii)
computes an occupancy map of the floor within a prescribed accuracy and quickly
eliminates unoccupied regions from the search space, and (iii) travels
efficiently to collect the required samples for target detection. We rigorously
analyze the algorithm and establish formal guarantees on the target detection
accuracy and the expected detection time. We illustrate the algorithm using a
simulated multi-target search scenario.

Robotics;Systems and Control;Systems and Control;Machine Learning