Comparing Bayesian and Montecarlo localization for a robot with local vision

Abstract

Position estimation is one of the classic problems in mobile robotics. The goal of this paper is to compare two probabilistic localization methods based on local vision for a mobile robot. The experimental set up is based on the Aibo league of the RoboCup, where the robotic dogs major sensor is the on-board camera. Two localization algorithms, Bayesian and Montecarlo (MCL), have been implemented and compared, and their behaviour studied in several situations using a simulator