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http://dx.doi.org/10.5302/J.ICROS.2006.12.1.047

Monte Carlo Localization for Mobile Robots Under REID Tag Infrastructures  

Seo Dae-Sung (과학기술연합대학원 대학교 가상공학)
Lee Ho-Gil (한국생산기술연구원 운동메카팀)
Kim Hong-Suck (한국생산기술연구원 제어지능팀)
Yang Gwang-Woong (한국생산기술연구원 제어지능팀)
Won Dae-Hee (한국생산기술연구원 센서인식팀)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.12, no.1, 2006 , pp. 47-53 More about this Journal
Abstract
Localization is a essential technology for mobile robot to work well. Until now expensive sensors such as laser sensors have been used for mobile robot localization. We suggest RFID tag based localization system. RFID tag devices, antennas and tags are cheap and will be cheaper in the future. The RFID tag system is one of the most important elements in the ubiquitous system and RFID tag will be attached to all sorts of goods. Then, we can use this tags for mobile robot localization without additional costs. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used for identifying mobile robot's location and pose in the smart floor. We discuss a number of challenges related to this approach, such as tag distribution (density and structure), typing and clustering. When a mobile robot localizes in this smart floor, the localization error mainly results from the sensing range of the RFID reader, because the reader just ran know whether a tag is in the sensing range of the sensor. So, in this paper, we suggest two algorithms to reduce this error. We apply the particle filter based Monte Carlo localization algorithm to reduce the localization error. And with simulations and experiments, we show the possibility of our particle filter based Monte Carlo localization in the RFID tag based localization system.
Keywords
localization; mobile robot; RFID; ubiquitous; particle filter; monte carlo localization;
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  • Reference
1 F. Dellaert, D. Fox, W. Burgard, S. Thrun 'Monte carlo localization for mobile robots,' Institute of Computer Science 3, University of Bonn bonn D-53117 Bohn
2 J. Brusey, M. Harrison, Ch. Floerkemeier, and M. Fletcher, 'Reasoning about uncertainty in location identification with RFID,' IJCAI-2003 Workshop on Reasoning with Uncertainty in Robotics, 2003
3 J. Bohn and F. Mattem, 'Super distributed RFID tag infrastructure,' Proceedings of the 2nd European Symposium on Ambient Intelligence, Springer-Verlag, pp. 1-12, 2004
4 D.-S. Seo, D.-H. Won, G.-W. Yanger, 'A probabilistic approach for mobile robot localization under RFID tag infrastructures,' ICASE 2005
5 D. Fox, W. Brugard, and S. Thrun, 'Markov localization for mobile robots in dynamic environments,' Journal of Artificial Intelligence Research 11, pp. 391-427, 1999
6 T. Nara and S. Ando, 'Localization of RFID tags from measurement of complex gradients of electromagnetic fields,' INSS 2004
7 H. Bruyninkx, 'Bayesian probability,' 2002
8 D. Hahnel, W. Burgard, D. Fox, K. Fishikin, and M. Philipose, 'Mapping and Localization with RFID Technology,' Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)., pp. 1015-1020, 2004   DOI