Fast Markov Localization using Angle-Histogram

Abstract

Localization is one of the most important issues for mobile robots since all tasks are commanded to a mobile robot based on the assumption that the mobile robot knows its position. Even though non-probabilistic techniques are faster than probabilistic approaches, those are sensitive to measurement errors and a mobile robot may lose its position in complex environments. On the contrary, probabilistic approaches have many advantages since those can cope with sensor noises and can globally localize a mobile robot. However, those probabilistic approaches are time-consuming techniques because of the heavy computational loads due to huge comparative data. In this paper, we propose a probabilistic localization method including global localization by remodeling raw laser sensory data using angle histogram to reduce computational loads for localization. Furthermore, exact heading angle of a mobile robot is determined in a fast manner. The algorithm is experimented successfully by using a mobile robot named KARA.