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

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.