Self-supervised monocular distance learning on a lightweight micro air vehicle
K. Lamers
Sjoerd Tijmons (TU Delft - Aerospace Engineering)
Christophe de Wagter (TU Delft - Aerospace Engineering)
Guido de Croon (TU Delft - Aerospace Engineering)
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Abstract
Obstacle detection by monocular vision is challenging because a single camera does not provide a direct measure for absolute distances to objects. A self-supervised learning approach is proposed that combines a camera and a very small short-range proximity sensor to find the relation between the appearance of objects in camera images and their corresponding distances. The method is efficient enough to run real time on a small camera system that can be carried onboard a lightweight MAV of 19 g. The effectiveness of the method is demonstrated by computer simulations and by experiments with the real platform in flight.