Effective application of Monte Carlo localization for service robot

Abstract

At indoor environment, a service robot must know where it is at any time. Thus, reliable position estimation is a basic and key problem. Probabilistic robotics techniques have become one of the dominant paradigms for algorithm design in robotics. Recent work on Monte Carlo Localization with particle-based density representation becomes popular. In this paper we introduce the multi-sensor based Monte Carlo localization (MCL) method which represents a robot's belief by a set of weighted samples and use the laser range finder (LRF) sensor to measurement update. The experiment results illustrate the effectivity and robust of MCL application for our service robot.