Uniform Monte Carlo localization - fast and robust self-localization method for mobile robots

Abstract

In this paper, we describe a novel self-localization algorithm. Self-localization methods are required for lowering the computational cost and handling vague sensor data. Thus, we propose to use only the uniform distribution to represent probability distributions in Monte Carlo localization, and name this method a uniform Monte Carlo localization (Uniform MCL). We manifest the low computational cost and robustness of Uniform MCL in the environment of RoboCup Sony legged robot league.