A Kalman-Filter-Based Method for Pose Estimation in Visual Servoing. Janabi-Sharifi, F. & Marey, M. IEEE Transactions on Robotics, 26(5):939 -947, 2010.
doi  abstract   bibtex   
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 #x201C;known #x201D; 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.
@Article{Janabi-Sharifi2010,
  Title                    = {A Kalman-Filter-Based Method for Pose Estimation in Visual Servoing},
  Author                   = {Janabi-Sharifi, F. and Marey, M.},
  Journal                  = {IEEE Transactions on Robotics},
  Year                     = {2010},
  Number                   = {5},
  Pages                    = {939 -947},
  Volume                   = {26},

  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 #x201C;known #x201D; 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.},
  Doi                      = {10.1109/TRO.2010.2061290},
  ISSN                     = {1552-3098},
  Keywords                 = {filtering},
  Review                   = {Hmm. It's for video-based motion capture},
  Timestamp                = {2010.10.09}
}
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