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A method for stereo-vision-based tracking for robotic applications

Published online by Cambridge University Press:  09 June 2009

Pubudu N. Pathirana ,
Adrian N. Bishop ,
Andrey V. Savkin ,
Samitha W. Ekanayake  and
Timothy J. Black
Pubudu N. Pathirana*
Affiliation:
School of Engineering and IT, Deakin University, Australia
Adrian N. Bishop
Affiliation:
School of Engineering and IT, Deakin University, Australia
Andrey V. Savkin
Affiliation:
School of Electrical Engineering and Telecommunications, University of New South Wales, Australia
Samitha W. Ekanayake
Affiliation:
School of Engineering and IT, Deakin University, Australia
Timothy J. Black
Affiliation:
School of Engineering and IT, Deakin University, Australia
*
*Corresponding author. E-mail: pubudu@deakin.edu.au
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Summary

Vision-based tracking of an object using perspective projection inherently results in non-linear measurement equations in the Cartesian coordinates. The underlying object kinematics can be modelled by a linear system. In this paper we introduce a measurement conversion technique that analytically transforms the non-linear measurement equations obtained from a stereo-vision system into a system of linear measurement equations. We then design a robust linear filter around the converted measurement system. The state estimation error of the proposed filter is bounded and we provide a rigorous theoretical analysis of this result. The performance of the robust filter developed in this paper is demonstrated via computer simulation and via practical experimentation using a robotic manipulator as a target. The proposed filter is shown to outperform the extended Kalman filter (EKF).

Keywords

Linear filteringSet-estimationStereo visionRobust filteringTarget tracking
Type
Article
Information
Robotica , Volume 28 , Issue 4 , July 2010 , pp. 517 - 524
Copyright
Copyright © Cambridge University Press 2009

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