A kalman-filter-based method for pose estimation in visual servoing

Authors:
Farrokh Janabi-Sharifi;Mohammed Marey
Affiliations:
Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, ON, Canada;IRISA, INRIA Rennes Bretagne Atlantique, Universitaire de Beaulieu, Rennes Cedex, France
Venue:
IEEE Transactions on Robotics
Year:
2010

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Abstract

The problem of estimating position and orientation (pose) of an object in real time constitutes an important issue for vision-based control of robots. Many vision-based pose-estimation schemes in robot control rely on an extended Kalman filter (EKF) that requires tuning of filter parameters. To obtain satisfactory results, EKF-based techniques rely on "known" noise statistics, initial object pose, and sufficiently high sampling rates for good approximation of measurement-function linearization. Deviations from such assumptions usually lead to degraded pose estimation during visual servoing. In this paper, a new algorithm, namely iterative adaptive EKF (IAEKF), is proposed by integrating mechanisms for noise adaptation and iterative-measurement linearization. The experimental results are provided to demonstrate the superiority of IAEKF in dealing with erroneous a priori statistics, poor pose initialization, variations in the sampling rate, and trajectory dynamics.