GEMA2:Geometrical matching analytical algorithm for fast mobile robots global self-localization

[EN] This paper presents a new algorithm for fast mobile robot self-localization in structured indoor environments based on geometrical and analytical matching, GEMA(2). The proposed method takes advantage of the available structural information to perform a geometrical matching with the environment information provided by measurements collected by a laser range finder. In contrast to other global self-localization algorithms like Monte Carlo or SLAM, GEMA(2) provides a linear cost with respect the number of measures collected, making it suitable for resource-constrained embedded systems. The proposed approach has been implemented and tested in a mobile robot with limited computational resources showing a fast converge from global self-localization. (C) 2014 Elsevier B.V. All rights reserved.This work has been partially funded by FEDER-CICYT projects with references DPI2011-28507-C02-01 and HAR2012-38391-C02-02 financed by Ministerio de Ciencia e Innovacion and Ministerio de Economia y Competitividad (Spain).Sánchez Belenguer, C.; Soriano Vigueras, Á.; Vallés Miquel, M.; Vendrell Vidal, E.; Valera Fernández, Á. (2014). GEMA2:Geometrical matching analytical algorithm for fast mobile robots global self-localization. Robotics and Autonomous Systems. 62(6):855-863. https://doi.org/10.1016/j.robot.2014.01.009S85586362