Sensor Selection by GMB-REM in Real 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), using a single sensor. 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 system for performing sensor selection is introduced, namely an enhanced form of GMB-REM. Empirical results show the new system outperforms GMB-REM using sonar alone. More specifically, it is able to select between multiple sensors at each robot"s position, and further minimises the average robot localisation error.