Robust Unscented Kalman Filter for Visual Servoing System

This paper presents a robust pose estimator for visual servoing system. Although various filters has been used as pose estimators, very limited research has been focused on the stability and robustness of pose estimators. UKF or EKF based pose estimator is one of most celebrated approaches in uncertain and noisy environment for nonlinear observations. However convergence of these filters is subject to some restrictive conditions in practice. In order to obtain a robust converging filter, pose estimation problem in visual servoing system is decomposed to an unscented Kalman observer (UKO) in cascade with a Kalman filter (KF). This structure inverts an uncertain nonlinear estimation problem to a certain nonlinear estimation in addition to an uncertain linear estimation. Additionally, a modified principal component analysis (PCA) based feature extractor is extended in this paper, which is shown to be robust in a noisy environment. The reported experimental results verify the effectiveness of the proposed structure in visual servoing system.