Extended Kalman and Particle Filtering for sensor fusion in
mobile robot localization
State estimation is a major problem in mobile robot localization. To this end gaussian and nonparametric
filters have been developed. In this paper the Extended Kalman Filter which assumes gaussian measurement noise is compared to the Particle Filter which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a mobile robot is used, when measurements are available from both odometric and sonar sensors. It is shown that in this kind of sensor fusion problem the Particle Filter outperforms
the Extended Kalman Filter, at the cost of more demanding computations.