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

作者：Janabi Sharifi Farrokh^*; Marey Mohammed

来源：IEEE Transactions on Robotics, 2010, 26(5): 939-947.

DOI：10.1109/TRO.2010.2061290

• Adaptation
• Kalman filter (KF)
• control
• pose estimation
• robotic manipulator
• visual servoing

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.

• 出版日期2010-10
• 单位INRIA