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- W2007687617 abstract "Computer vision has been an active field of research for many decades; it has also become widely used for airborneapplications in the last decade or two. Much airborne computer vision research has focused on navigation for UnmannedAir Vehicles; this paper presents a method to estimate the full 3D position information of a UAV by integrating visualcues from one single image with data from an Inertial Measurement Unit under the Kalman Filter formulation. Previouswork on visual 3D position estimation for UAV landing has been achieved by using 2 or more frames of image data withfeature enriched information in the image; however raw vision state estimates are hugely suspect to image noise. Thispaper uses a rather conventional type of landing pad with visual features extracted for use in the Kalman filter to obtainoptimal 3D position estimates. This methodology promises to provide state estimates that are better suited for guidanceand control of a UAV. This also promise autonomous landing of UAVs without GPS information to be conducted. Theresult of this implementation tested with flight images is presented." @default.
- W2007687617 created "2016-06-24" @default.
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- W2007687617 date "2007-09-09" @default.
- W2007687617 modified "2023-09-23" @default.
- W2007687617 title "Terminal phase visual position estimation for a tail-sitting vertical takeoff and landing UAV via a Kalman filter" @default.
- W2007687617 doi "https://doi.org/10.1117/12.733453" @default.
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