Self-supervised monocular distance learning on a lightweight micro air vehicle

Lamers, K.; Tijmons, S.; de Wagter, C.; de Croon, G.C.H.E.

doi:10.1109/IROS.2016.7759284

Self-supervised monocular distance learning on a lightweight micro air vehicle

Conference paper (2016)

Authors

K. Lamers

S. Tijmons

C. de Wagter

G.C.H.E. de Croon

DOI: https://doi.org/10.1109/IROS.2016.7759284

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Published Date

2016

Language

English

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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.

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