Abstract
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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