<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Marta Marron</style></author><author><style face="normal" font="default" size="100%">Garcia, Juan Carlos</style></author><author><style face="normal" font="default" size="100%">Miguel Angel Sotelo</style></author><author><style face="normal" font="default" size="100%">Elena Lopez</style></author><author><style face="normal" font="default" size="100%">Manuel Mazo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fusing odometric and vision data with an EKF to estimate the absolute position of an autonomous mobile robot</style></title><secondary-title><style face="normal" font="default" size="100%">ETFA '03, IEEE Conference on Emerging Technologies and Factory Automation, 2003.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2003</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&amp;arnumber=1247760&amp;isnumber=27939</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA</style></publisher><pub-location><style face="normal" font="default" size="100%">Lisboa (Portugal)</style></pub-location><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">591 - 596</style></pages><isbn><style face="normal" font="default" size="100%">0-7803-7937-3</style></isbn><language><style face="normal" font="default" size="100%">English</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>