Robust mobile robot visual tracking control system using self-tuning Kalman filter

Chi Yi Tsai*, Kai-Tai Song, Xavier Dutoit, Hendrik Van Brussel, Marnix Nuttin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007
Pages161-166
Number of pages6
DOIs
StatePublished - 9 Oct 2007
Event2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007 - Jacksonville, FL, United States
Duration: 20 Jun 200723 Jun 2007

Publication series

NameProceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007

Conference

Conference2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007
CountryUnited States
CityJacksonville, FL
Period20/06/0723/06/07

Keywords

  • Self-tuning Kalman filter
  • System modelling
  • Visual estimation
  • Visual tracking control

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