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The evolved Gaussian mixture Bayes' technique using sensor selection task integrated with sensor fusion scheme in mobile robot position estimation

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1 Author(s)
Koshizen, T. ; Dept. of Syst. Eng., Australian Nat. Univ., Canberra, ACT, Australia

Modelling and reducing uncertainty are two essential problems with mobile robot localisation. Previously we developed a robot localisation system, namely the Gaussian Mixture of Bayes with Regularised Expectation Maximisation (GMB-REM), which introduced sensor selection task. GMB-REM allows a robot's position to be modelled as a probability distribution, and uses Bayes' theorem to reduce the uncertainty of its location. In this paper, a new sensor selection task incorporated with sensor fusion is proposed, namely an evolved form of GMB-REM. Empirical results show the new sensor selection method outperforms GMB-REM with the previous sensor selection. Especially, in this paper, we illustrate that the new system is able to significantly constrain the error of a robot's position

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Computational Intelligence in Robotics and Automation, 1999. CIRA '99. Proceedings. 1999 IEEE International Symposium on

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