  This paper considers the problem of finding a landing spot for a drone in a
dense urban environment. The conflicting requirement of fast exploration and
high resolution is solved using a multi-resolution approach, by which visual
information is collected by the drone at decreasing altitudes so that spatial
resolution of the acquired images increases monotonically. A probability
distribution is used to capture the uncertainty of the decision process for
each terrain patch. The distributions are updated as information from different
altitudes is collected. When the confidence level for one of the patches
becomes larger than a pre-specified threshold, suitability for landing is
declared. One of the main building blocks of the approach is a semantic
segmentation algorithm that attaches probabilities to each pixel of a single
view. The decision algorithm combines these probabilities with a priori data
and previous measurements to obtain the best estimates. Feasibility is
illustrated by presenting a number of examples generated by a realistic
closed-loop simulator.
