International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Mobile Robot Localization and Mapping using a Gaussian Sum Filter

  • Kwok, Ngai Ming (ARC Centre of Excellence for Autonomous Systems (CAS), Faculty of Engineering, University of Technology) ;
  • Ha, Quang Phuc (ARC Centre of Excellence for Autonomous Systems (CAS), Faculty of Engineering, University of Technology) ;
  • Huang, Shoudong (ARC Centre of Excellence for Autonomous Systems (CAS), Faculty of Engineering, University of Technology) ;
  • Dissanayake, Gamini (ARC Centre of Excellence for Autonomous Systems (CAS), Faculty of Engineering, University of Technology) ;
  • Fang, Gu (School of Engineering, University of Western Sydney)
  • Published : 2007.06.30
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Abstract

A Gaussian sum filter (GSF) is proposed in this paper on simultaneous localization and mapping (SLAM) for mobile robot navigation. In particular, the SLAM problem is tackled here for cases when only bearing measurements are available. Within the stochastic mapping framework using an extended Kalman filter (EKF), a Gaussian probability density function (pdf) is assumed to describe the range-and-bearing sensor noise. In the case of a bearing-only sensor, a sum of weighted Gaussians is used to represent the non-Gaussian robot-landmark range uncertainty, resulting in a bank of EKFs for estimation of the robot and landmark locations. In our approach, the Gaussian parameters are designed on the basis of minimizing the representation error. The computational complexity of the GSF is reduced by applying the sequential probability ratio test (SPRT) to remove under-performing EKFs. Extensive experimental results are included to demonstrate the effectiveness and efficiency of the proposed techniques.

Keywords

References

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