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One of the main problems in the field of mobile robotics is the estimation of the robot's position in the environment. Position probability grids have been proven to be a robust technique for the estimation of the absolute position of a mobile robot. In this paper we describe an application of position probability grids to the tracking of the position of the robot. The main difference of our method to previous approaches lies in the fact that the position probability grid technique is a Bayesian approach which is able to deal with noisy sensors as well as ambiguities and is able to integrate sensor readings of different types of sensors over time. Given a starting position this method estimates the robot's current position by matching sensor readings against a metric model of the environment. Results described in this paper illustrate the robustness of this method against noisy sensors and errors in the environmental model.
Burgard et al. (Mon,) studied this question.