標題: Learning-based risk assessment and motion estimation by vision for unmanned aerial vehicle landing in an unvisited area
作者: Cheng, Hsiu-Wen
Chen, Tsung-Lin
Tien, Chung-Hao
機械工程學系
光電工程學系
Department of Mechanical Engineering
Department of Photonics
關鍵字: optical flow;self-supervised learning;unmanned aerial vehicle;vision-based landing
公開日期: 1-Nov-2019
摘要: We proposed a vision-based methodology as an aid for an unmanned aerial vehicle (UAV) landing on a previously unsurveyed area. When the UAV was commanded to perform a landing mission in an unknown airfield, the learning procedure was activated to extract the surface features for learning the obstacle appearance. After the learning process, while hovering the UAV above the potential landing spot, the vision system would be able to predict the roughness value for confidence in a safe landing. Finally, using hybrid optical flow technology for motion estimation, we successfully carried out the UAV landing without a predefined target. Our work combines a well-equipped flight control system with the proposed vision system to yield more practical versatility for UAV applications. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.
URI: http://dx.doi.org/10.1117/1.JEI.28.6.063011
http://hdl.handle.net/11536/153480
ISSN: 1017-9909
DOI: 10.1117/1.JEI.28.6.063011
期刊: JOURNAL OF ELECTRONIC IMAGING
Volume: 28
Issue: 6
起始頁: 0
結束頁: 0
Appears in Collections:Articles