Progress of Probabilistic Localization Methods in Mobile Robots

Abstract

This paper overviews some popular mobile robot probabilistic localization methods in recent years, analyzes and compares the performances of these methods. All of these methods employ the Bayesian rule as a fundamental theory. Firstly, we introduce the Kalman filter which is extensively used in position tracking, and the Markov localization method which has made many successes in global localization. Secondly, the Monte Carlo method is presented, which uses a particle filter technique and are more efficient computationally. The most recently used adaptive sampling methods are also introduced, and they have demonstrated much better results than the simple particle filter approaches. At last, the key technologies of probabilistic localization methods are analyzed, and the trends of research in the future are discussed.