<?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%">Álvaro Hernández</style></author><author><style face="normal" font="default" size="100%">E. García</style></author><author><style face="normal" font="default" size="100%">Garcia, Juan Carlos</style></author><author><style face="normal" font="default" size="100%">Daniel Ruiz</style></author><author><style face="normal" font="default" size="100%">María del Carmen Pérez</style></author><author><style face="normal" font="default" size="100%">R. Sanz</style></author><author><style face="normal" font="default" size="100%">Ureña, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Particle filter for merging internal and external positioning information in mobile robot navigation</style></title><secondary-title><style face="normal" font="default" size="100%">2012 International IEEE Intelligent Vehicles Symposium (IV 2012)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2012</style></date></pub-dates></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://geintra-uah.org/system/files/iv2012-odomus-dani_in-proceedings_publicoweb.pdf</style></url></related-urls></urls><pages><style face="normal" font="default" size="100%">1-6</style></pages><isbn><style face="normal" font="default" size="100%">978-84-695-3472-4</style></isbn><language><style face="normal" font="default" size="100%">English</style></language><abstract><style face="normal" font="default" size="100%">This paper reports a local positioning systems for mobile robots based on merging the information from the internal odometer sensor of the mobile robot and the measurements from a set of external ultrasonic beacons. The cumulative errors of the odometer localization are corrected by using the positioning data obtained with an ultrasonic local positioning system (ULPS). It consists of four beacons placed at known positions of the ceiling, all of them emitting periodically and simultaneously. To avoid interferences among the simultaneous emissions, Direct Sequence Code Division Multiple Access (DS-CDMA) techniques have been used. The arrival instant from each beacon emission is detected by a
mobile robot, which obtains its position by hyperbolic trilateration. Then, a Bayesian localization technique(implemented by a particle filter) is used to merge these measurements with those obtained with the odometer and produce a better estimation of the robot’s position.</style></abstract></record></records></xml>