NDLI: Robust Mobile Robot Visual Tracking Control System Using Self-Tuning Kalman Filter

Content Provider	IEEE Xplore Digital Library
Author	Chi-Yi Tsai Kai-Tai Song Dutoit, X. Van Brussel, H. Nuttin, M.
Copyright Year	2007
Description	Author affiliation: Nat. Chiao Tung Univ., Hsinchu (Chi-Yi Tsai; Kai-Tai Song)
Abstract	This paper presents a novel design of a robust visual tracking control system, which consists of a visual tracking controller and a visual state estimator. This system facilitates human-robot interaction of a unicycle-modeled mobile robot equipped with a tilt camera. Based on a novel dual-Jacobian visual interaction model, a dynamic motion target can be tracked using a single visual tracking controller without target's 3D velocity information. The visual state estimator aims to estimate the optimal system state and target image velocity, which is used later by the visual tracking controller. To achieve this, a self-tuning Kalman filter is proposed to estimate interesting parameters online in real-time. Further, because the proposed method is fully working in image space, the computational complexity and the sensor/camera modeling errors can be reduced. Experimental results validate the effectiveness of the proposed method, in terms of tracking performance, system convergence, and robustness.
Starting Page	161
Ending Page	166
File Size	1507875
Page Count	6
File Format	PDF
ISBN	1424407893
DOI	10.1109/CIRA.2007.382860
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robust control Mobile robots Robot control Control systems Target tracking State estimation Cameras Velocity control Robot vision systems Motion control self-tuning Kalman filter System modelling visual tracking control visual estimation
Content Type	Text
Resource Type	Article

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