Robust unscented Kalman filter for visual servoing system
作者： M. Salehian , S. RayatDoost , H. D. Taghirad
DOI: 10.1109/ICCIAUTOM.2011.6356799
关键词: Computer vision 、 Unscented transform 、 Nonlinear system 、 Estimator 、 Visual servoing 、 Control theory 、 Extended Kalman filter 、 Artificial intelligence 、 Computer science 、 Robustness (computer science) 、 Pose 、 Kalman filter
摘要: Abstract — This paper presents arobust pose estimator for visual servoing system. Although various filters has been used as estimators, very limited research focused on the stability and robustness of estimators. UKF or EKF based is one most celebrated approaches in uncertain noisy environment nonlinear observations. However convergence these subject to some restrictive conditions practice. In order obtain a robust converging filter, estimation problem system decomposed an unscented Kalman observer (UKO) cascade with filter (KF). structure inverts certain addition linear estimation. Additionally, modified principal component analysis (PCA) feature extractor extended this paper, which shown be environment. The reported experimental results verify effectiveness proposed
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