Micro Air Vehicles (MAVs) to detect obstacles 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 motion 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 regression function is learned that maps appearance features represented by texton distributions to a surface 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.
Self-supervised learning has also been demonstrated for the landing task of an MAV REF .
Optical-Flow based Self-Supervised Learning of Obstacle Appearance applied to MAV Landing
ArXiv
ArXiv
Engineering Computer Science