Print Email Facebook Twitter Self-supervised monocular distance learning on a lightweight micro air vehicle Title Self-supervised monocular distance learning on a lightweight micro air vehicle Author Lamers, K. Tijmons, S. (TU Delft Control & Simulation) de Wagter, C. (TU Delft Control & Simulation) de Croon, G.C.H.E. (TU Delft Control & Simulation) Date 2016 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. To reference this document use: http://resolver.tudelft.nl/uuid:fd86be3e-b806-45ac-ba12-e447b255df00 DOI https://doi.org/10.1109/IROS.2016.7759284 Embargo date 2017-11-01 ISBN 978-1-5090-3762-9 Source IROS 2016: 2016 IEEE/RSJ International Conference Event 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, 2016-10-09 → 2016-10-14, Daejeon, Korea, Republic of Part of collection Institutional Repository Document type conference paper Rights © 2016 K. Lamers, S. Tijmons, C. de Wagter, G.C.H.E. de Croon Files PDF IROS_Kevin_Revision.pdf 2.33 MB Close viewer /islandora/object/uuid:fd86be3e-b806-45ac-ba12-e447b255df00/datastream/OBJ/view