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Robust gaze-steering of an active vision system against errors in the estimated parameters

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  • Youngmo Han

Abstract

Gaze-steering is often used to broaden the viewing range of an active vision system. Gaze-steering procedures are usually based on estimated parameters such as image position, image velocity, depth and camera calibration parameters. However, there may be uncertainties in these estimated parameters because of measurement noise and estimation errors. In this case, robust gaze-steering cannot be guaranteed. To compensate for such problems, this paper proposes a gaze-steering method based on a linear matrix inequality (LMI). In this method, we first propose a proportional derivative (PD) control scheme on the unit sphere that does not use depth parameters. This proposed PD control scheme can avoid uncertainties in the estimated depth and camera calibration parameters, as well as inconveniences in their estimation process, including the use of auxiliary feature points and highly non-linear computation. Furthermore, the control gain of the proposed PD control scheme on the unit sphere is designed using LMI such that the designed control is robust in the presence of uncertainties in the other estimated parameters, such as image position and velocity. Simulation results demonstrate that the proposed method provides a better compensation for uncertainties in the estimated parameters than the contemporary linear method and steers the gaze of the camera more steadily over time than the contemporary non-linear method.

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  • Youngmo Han, 2015. "Robust gaze-steering of an active vision system against errors in the estimated parameters," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(2), pages 355-363, January.
    • Handle: RePEc:taf:tsysxx:v:46:y:2015:i:2:p:355-363
    DOI: 10.1080/00207721.2013.783948
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    References listed on IDEAS

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    1. Yeguo Sun & Shiyin Qin, 2011. "Stability analysis and controller design for networked control systems with random time delay," International Journal of Systems Science, Taylor & Francis Journals, vol. 42(3), pages 359-367.
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