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

    TitleFusing odometric and vision data with an EKF to estimate the absolute position of an autonomous mobile robot
    Publication TypeConference Paper
    Año de publicación2003
    AutoresMarron, M, Garcia, JC, Sotelo, MA, Lopez, E, Mazo, M
    Idioma de publicaciónEnglish
    Conference NameETFA '03, IEEE Conference on Emerging Technologies and Factory Automation, 2003.
    Volumen1
    Páginas591 - 596
    EditorialIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
    Conference LocationLisboa (Portugal)
    Fecha de publicación09/2003
    Numero ISBN0-7803-7937-3
    DOI10.1109/ETFA.2003.1247760
    URLhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1247760&isnumber=27939
    Abstract

    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.