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Fusing odometric and vision data with an EKF to estimate the absolute position of an autonomous mobile robot

Título	Fusing odometric and vision data with an EKF to estimate the absolute position of an autonomous mobile robot
Tipo de publicación	Conference Paper
Año de publicación	2003
Autores	Marron, M, Garcia, JC, Sotelo, MA, Lopez, E, Mazo, M
Idioma de publicación	English
Conference Name	ETFA '03, IEEE Conference on Emerging Technologies and Factory Automation, 2003.
Volumen	1
Páginas	591 - 596
Editorial	IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Conference Location	Lisboa (Portugal)
Fecha de publicación	09/2003
Numero ISBN	0-7803-7937-3
DOI	10.1109/ETFA.2003.1247760
URL	http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1247760&isnumber=27939
Resumen	This paper presents the development of a probabilistic algorithm based on an Extended Kalman Filter (EKF), used to estimate the absolute position of an indoor autonomous robot. With EKF it is possible to fuse relative and absolute positioning data, including some kind of uncertainty related to sensory systems. To reach this objective it is necessary to do an important model analysis to enable the on-line adaptation of the estimation algorithm. The development presented in this paper has been designed for an autonomous wheelchair, whose real-time and reliability constraints have to be taken into account in the algorithm.

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