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Sharifi10a

F. Janabi-Sharifi, M. Marey. A Kalman-Filter-Based Method for Pose Estimation in Visual Servoing. IEEE Trans. on Robotics, 26(5):939-947, October 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

BibTex Reference

@article{Sharifi10a,
   Author = {Janabi-Sharifi, F. and Marey, M.},
   Title = {A Kalman-Filter-Based Method for Pose Estimation in Visual Servoing},
   Journal = {IEEE Trans. on Robotics},
   Volume = {    26},
   Number = {5},
   Pages = {939--947},
   Publisher = {IEEE},
   Month = {October},
   Year = {2010}
}

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