A dynamic size MCL algorithm for mobile robot localization

Abstract

Mobile robot localization is a very important problem in robotics as most robot's tasks need the positional information. Monte Carlo Localization(MCL) is one of the most popular and efficient localization algorithms for mobile robot localization. MCL algorithm represents a robot's pose by a set of weighted particles. In order to further improve the performance of MCL, many extensions have been proposed. In this paper, we proposed an algorithm called dynamic size MCL, an extension of MCL. We incorporate the clustering approach into traditional MCL. With the help of clustering information, our algorithm could reduce the number of particles during the process of localization, which lower the computational cost. Experimental results demonstrate the effectiveness of the proposed method.