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A method for stereo-vision based tracking for robotic applications

Pathirana, Pubudu N., Bishop, Adrian N., Savkin, Andrey V., Ekanayake, Samitha W. and Black, Timothy J. 2008, A method for stereo-vision based tracking for robotic applications, in CDC 2008 : Proceedings of the 47th IEEE Conference on Decision and Control, IEEE, Piscataway, N.J., pp. 1298-1303.

Document type: Conference Paper
Collection: School of Engineering and Information Technology
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Title A method for stereo-vision based tracking for robotic applications
Author(s) Pathirana, Pubudu N.
Bishop, Adrian N.
Savkin, Andrey V.
Ekanayake, Samitha W.
Black, Timothy J.
Conference name IEEE Conference of Decision and Control (47th : 2008 : Cancun, Mexico)
Conference location Cancun, Mexico
Conference dates 9-11 December 2008
Title of proceedings CDC 2008 : Proceedings of the 47th IEEE Conference on Decision and Control
Editor(s) [Unknown]
Publication date 2008
Conference series IEEE Conference on Decision and Control
Start page 1298
End page 1303
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Vision based tracking of an object using the ideas of perspective projection inherently consists of nonlinearly modelled measurements although the underlying dynamic system that encompasses the object and the vision sensors can be linear. Based on a necessary stereo vision setting, we introduce an appropriate measurement conversion techniques which subsequently facilitate using a linear filter. Linear filter together with the aforementioned measurement conversion approach conforms a robust linear filter that is based on the set values state estimation ideas; a particularly rich area in the robust control literature. We provide a rigorously theoretical analysis to ensure bounded state estimation errors formulated in terms of an ellipsoidal set in which the actual state is guaranteed to be included to an arbitrary high probability. Using computer simulations as well as a practical implementation consisting of a robotic manipulator, we demonstrate our linear robust filter significantly outperforms the traditionally used extended Kalman filter under this stereo vision scenario.
ISBN 9781424431243
Language eng
Field of Research 090602 Control Systems
Socio Economic Objective 861302 Automotive Equipment
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30018205
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