@conference {254,
	title = {Fusing odometric and vision data with an EKF to estimate the absolute position of an autonomous mobile robot},
	booktitle = {ETFA {\textquoteright}03, IEEE Conference on Emerging Technologies and Factory Automation, 2003.},
	volume = {1},
	year = {2003},
	month = {09/2003},
	pages = {591 - 596},
	publisher = {IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA},
	organization = {IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA},
	address = {Lisboa (Portugal)},
	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.},
	isbn = {0-7803-7937-3},
	doi = {10.1109/ETFA.2003.1247760},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=1247760\&isnumber=27939},
	author = {Marta Marron and Garcia, Juan Carlos and Miguel Angel Sotelo and Elena Lopez and Manuel Mazo}
}
