  Monocular optical flow has been widely used to detect obstacles in Micro Air
Vehicles (MAVs) during visual navigation. However, this approach requires
significant movement, which reduces the efficiency of navigation and may even
introduce risks in narrow spaces. In this paper, we introduce a novel setup of
self-supervised learning (SSL), in which optical flow cues serve as a scaffold
to learn the visual appearance of obstacles in the environment. We apply it to
a landing task, in which initially 'surface roughness' is estimated from the
optical flow field in order to detect obstacles. Subsequently, a linear
regression function is learned that maps appearance features represented by
texton distributions to the roughness estimate. After learning, the MAV can
detect obstacles by just analyzing a still image. This allows the MAV to search
for a landing spot without moving. We first demonstrate this principle to work
with offline tests involving images captured from an on-board camera, and then
demonstrate the principle in flight. Although surface roughness is a property
of the entire flow field in the global image, the appearance learning even
allows for the pixel-wise segmentation of obstacles.
