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Proceedings Paper

Runway detection of an unmanned landing aerial vehicle based on vision
Author(s): Hongqun Wang; Jiaxiong Peng; Lingling Li
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Paper Abstract

When an monocular vision-based unmanned aerial vehicle (UAV) based on vision is flown to the final approach fix to intercept the glide slope without the navigation of Global Positioning System (GPS), the position and orientation of the airport runway in image must be detected accurately so as to a host of suitable procedures have to be followed. The optimum length of the final approach is about five miles from the runway threshold. The front view of the runway, which is achieved at the moment, is very illegible. The approaching marking (cross bar) of the runway are showed as some white spots of high intensity and the complicated backgrounds of the airport are included in the images. In this case, spots with high intensity should be extracted and classified, some of these spots are just the images of the background noises and the pseudo-targets, which can't be separated with the spots of the runway as in the view there is no significant characteristic difference among them ostensibly. Fortunately, in the terrestrial coordinate space, most of the runway marks are located at the apexes of a rectangle, having some geometric relationships. The relationship among the projection coordinates of the runway spots in the images can be determined according to the perspective principle, the constraint condition of the rectangle as well as the front shot constraint condition of the target, by using this relationship, the runway approaching marks can be separated, the position and the direction of the runway in the images can be identified. In this paper, the clustering management is adopted so as to greatly reduce the computing time. The consequence of the experiments shows that by this algorithm, even from a place far away from the runway whose marks are unclear, we also can effectively detect the runway.

Paper Details

Date Published: 3 November 2005
PDF: 10 pages
Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 60432A (3 November 2005); doi: 10.1117/12.654995
Show Author Affiliations
Hongqun Wang, Huazhong Univ. of Science and Technology (China)
Jiaxiong Peng, Huazhong Univ. of Science and Technology (China)
Lingling Li, Huazhong Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 6043:
MIPPR 2005: SAR and Multispectral Image Processing
Liangpei Zhang; Jianqing Zhang; Mingsheng Liao, Editor(s)

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