A method for stereo-vision based tracking for robotic applications
Date
2008
Authors
Pathirana, Pubudu
Bishop, Adrian
Savkin, Andrey V
Ekanayake , Samitha W.
Black, Timothy J.
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Publisher
Institute of Electrical and Electronics Engineers (IEEE Inc)
Abstract
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.
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Keywords
Keywords: Bounded state; High probabilities; Linear filters; Measurement conversions; Measurement-conversion approaches; Perspective projections; Practical implementations; Robotic applications; Robotic manipulators; Robust filters; Underlying dynamics; Vision sens
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Source
Proceedings of IEEE Conference on Decision and Control 2008
Type
Conference paper
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Restricted until
2037-12-31
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