<?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%">Daniel Pizarro</style></author><author><style face="normal" font="default" size="100%">Marta Marron</style></author><author><style face="normal" font="default" size="100%">Daniel Peon</style></author><author><style face="normal" font="default" size="100%">Manuel Mazo</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%">Enrique Santiso</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Robot and Obstacles Localization and Tracking with an External Camera Ring</style></title><secondary-title><style face="normal" font="default" size="100%">2008 IEEE International Conference on Robotics and Automation (ICRA08)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4543259&amp;isnumber=4543169</style></url></web-urls><related-urls><url><style face="normal" font="default" size="100%">https://mail.geintra-uah.org/en/system/files/2008-icra-pizarro.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Pasadena, California (USA)</style></pub-location><pages><style face="normal" font="default" size="100%">516-521</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4244-1647-9</style></isbn><language><style face="normal" font="default" size="100%">English</style></language><abstract><style face="normal" font="default" size="100%">In this paper a ring of calibrated and synchronized&lt;br /&gt;
cameras is used for achieving robot and obstacle localization&lt;br /&gt;
inside a common observed area. To avoid complex appearance&lt;br /&gt;
matching derived from the wide-baseline arrangement of cameras,&lt;br /&gt;
a metric occupancy grid is obtained by intersection of&lt;br /&gt;
silhouettes projected onto the floor. A particle filter is proposed&lt;br /&gt;
for tracking multiple objects by using the grid as observation&lt;br /&gt;
data. A clustering algorithm is included in the filter to increase&lt;br /&gt;
the robustness and adaptability of the multimodal estimation&lt;br /&gt;
task. To preserve identity of the robot from the set of tracked&lt;br /&gt;
objects, odometry readings are used to compute a Maximum&lt;br /&gt;
Likelihood (ML) global trajectory identification. As a proof of&lt;br /&gt;
concept, real results are obtained in a long sequence with a&lt;br /&gt;
mobile robot moving in a human-cluttered scene.&lt;/p&gt;</style></abstract></record></records></xml>