%0 Journal Article 
%F Sharifi10a
%A Janabi-Sharifi, F.
%A Marey, M.
%T A Kalman-Filter-Based Method for Pose Estimation in Visual Servoing
%J IEEE Trans. on Robotics
%V 	 26
%N 5
%P 939-947
%I IEEE
%X 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
%U http://www.irisa.fr/lagadic/pdf/2010_itro_sharifi.pdf
%U http://dx.doi.org/10.1109/TRO.2010.2061290
%8 October
%D 2010