Particle filter based robust mobile robot localization

Abstract

Mobile robot localization is an important issue in the service robotics area, which is to determine the position of a robot given a map of its environment. In this paper, we described a robust self-localization approach for mobile robot based on particle filtering, which is a sophisticated model for robust estimation. In this method a large number of hypothetical current particles are initially generated to represent the possible robot position, with each sensor update, the probability that each hypothetical particle is updated based on Bayesian principle. Similarly, every robot motion is also applied in a statistical way to the particles based on the statistical motion model. Experimental results demonstrated good performance and robustness of our approach.