Sensor fusion of odometry and sonar sensors by the Gaussian mixture Bayes' technique in mobile robot position estimation

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), using sonar sensors. 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 fusion is introduced, namely an enhanced form of GMB-REM. Empirical results show that the new system outperforms GMB-REM using sonar alone. More specifically, it is able to constrain the error of robot's positions even when sonar signals are noisy.