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    A Vision-Based Algorithm for UAV State Estimation During Vehicle Recovery

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    Burns_ku_0099M_11779_DATA_1.pdf (3.765Mb)
    Issue Date
    2011-08-31
    Author
    Burns, William Robert
    Publisher
    University of Kansas
    Format
    93 pages
    Type
    Thesis
    Degree Level
    M.S.
    Discipline
    Aerospace Engineering
    Rights
    This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
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    Abstract
    A computer vision-based algorithm for Unmanned Aerial Vehicle state estimation during vehicle recovery is presented. The algorithm is intended to be used to augment or back up Global Positioning System as the primary means of navigation during vehicle recovery for UAVs. The method requires a clearly visible recovery target with markers placed on the corners in addition to known target geometry. The algorithm uses clustering techniques to identify the markers, a Canny Edge detector and a Hough Transform to verify these markers actually lie on the recovery target, an optimizer to match the detected markers with coordinates in three-space, a non-linear transformation and projection solver to observe the position and orientation of the camera, and an Extended Kalman Filter (EKF) to improve the tracking of the state estimate. While it must be acknowledged that the resolution of the test images used is much higher than the resolution of images used in previous algorithms and that the images used to test this algorithm are either synthetic or taken in static conditions, the algorithm presented does give much better state estimates than previously-developed vision systems.
    URI
    http://hdl.handle.net/1808/10688
    Collections
    • Engineering Dissertations and Theses [1055]
    • Theses [3827]

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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