Fast Markov Localization in Indoor Environment Using SFM Angle-Histogram

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. And most simple features need additional information to represent the characteristics of 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 fast probabilistic localization method including global localization by remodeling raw laser sensory data using angle histogram to reduce computational loads for localization. The algorithm is experimented successfully by using a mobile robot named KARA.