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Proceedings Paper

Terminal phase visual position estimation for a tail-sitting vertical takeoff and landing UAV via a Kalman filter
Author(s): Allen C. Tsai; Peter W. Gibbens; R. Hugh Stone
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Paper Abstract

Computer vision has been an active field of research for many decades; it has also become widely used for airborne applications in the last decade or two. Much airborne computer vision research has focused on navigation for Unmanned Air Vehicles; this paper presents a method to estimate the full 3D position information of a UAV by integrating visual cues from one single image with data from an Inertial Measurement Unit under the Kalman Filter formulation. Previous work on visual 3D position estimation for UAV landing has been achieved by using 2 or more frames of image data with feature enriched information in the image; however raw vision state estimates are hugely suspect to image noise. This paper uses a rather conventional type of landing pad with visual features extracted for use in the Kalman filter to obtain optimal 3D position estimates. This methodology promises to provide state estimates that are better suited for guidance and control of a UAV. This also promise autonomous landing of UAVs without GPS information to be conducted. The result of this implementation tested with flight images is presented.

Paper Details

Date Published: 10 September 2007
PDF: 10 pages
Proc. SPIE 6764, Intelligent Robots and Computer Vision XXV: Algorithms, Techniques, and Active Vision, 67640P (10 September 2007); doi: 10.1117/12.733453
Show Author Affiliations
Allen C. Tsai, The Univ. of Sydney (Australia)
Peter W. Gibbens, The Univ. of Sydney (Australia)
R. Hugh Stone, The Univ. of Sydney (Australia)

Published in SPIE Proceedings Vol. 6764:
Intelligent Robots and Computer Vision XXV: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall; Juha Röning, Editor(s)

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