Sensor fusion of odometry and sonar sensors by the Gaussian mixture Bayes' technique in mobile robot position estimation
Takamasa Koshizen, Peter L. Bartlett, Alex Zelinsky · 2003
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.