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Journal Articles IEEE Transactions on Robotics Year : 2010

A Kalman-Filter-Based Method for Pose Estimation in Visual Servoing

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.
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Dates and versions

inria-00549107 , version 1 (21-12-2010)

Identifiers

  • HAL Id : inria-00549107 , version 1

Cite

F. Janabi-Sharifi, M. Marey. A Kalman-Filter-Based Method for Pose Estimation in Visual Servoing. IEEE Transactions on Robotics, 2010, 26 (5), pp.939-947. ⟨inria-00549107⟩
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