in: Proceedings of the 1st Euromicro Workshop on Advanced Mobile Robots, Kaiserslautern, Germany, 1996.
Abstract
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.