[1]HAI Dan,LI Yong,ZHANG Hui,et al.Simultaneous localization and mapping of a mobile robot in wireless sensor networks based on particle filtering[J].CAAI transactions on intelligent systems,2010,5(5):425-431.[doi:10.3969/j.issn.1673-4785.2010.05.008]
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Simultaneous localization and mapping of a mobile robot in wireless sensor networks based on particle filtering

CAAI transactions on intelligent systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 5 Number of periods: 2010 5 Page number: 425-431 Column: 学术论文—智能系统 Public date: 2010-10-25
Title:
Simultaneous localization and mapping of a mobile robot in wireless sensor networks based on particle filtering
Author(s):
HAI Dan;  LI Yong;  ZHANG Hui;  LI Xun
College of Mechatronics Engineering and Automation， National University of Defense Technology, Changsha 410073, China
Keywords:
wireless sensor network;  mobile robot;  SLAM;  particle filtering
CLC:
TP24
DOI:
10.3969/j.issn.1673-4785.2010.05.008
Abstract:
Localization is one of the most fundamental problems in mobile robots. A method for simultaneous localization and mapping (SLAM) in robot and WSN systems using range-only measurements was presented in a Bayes framework and a particle filtering method was designed for the problem. The estimations of the robot’s path and WSN node position were set to be clusters of particles which were called status particles. The status particles were used to estimate the whole state posterior by its position and weight. The algorithm assumed position of WSN nodes which were conditioned independently along the robot’s path, so the system posterior could be computed separately. A particle filter called a robot filter was used to compute the robot’s posterior and a separate copy of each node filter corresponding to each robot particle. Due to the low dimension of range measurement, methods were made for utilizing this information. The experiment proved the efficiency and practicality of the algorithm.
References:
［1］DURRANT-WHYTE H. Where am I? A tutorial on mobile vehicle localization［J］. Industrial Robot, 1994, 21(2): 11-16.
［2］DURRANT-WHYTE H，BAILEY T. Simultaneous localization and mapping: part Ⅰthe essential algorithms［J］. IEEE Robotics and Automation Magazine, 2006, 13(2): 99-108.
［3］RABAEY J M, AMMER M J, Da SILVA J L， et al. PicoRadio supports ad hocultra-low power wireless networking［J］. IEEE Computer, 2002, 33(7): 42-48.
［4］SAYED A H, TARIGHAT A, KHAJEHNOURI N. Network-based wireless location［J］. IEEE Signal Processing Magazine, 2005, 22(4): 24-40.
［5］SUN Guolin, CHEN Jie, GUO Wei, et al. Signal processing techniques in networkaided position［J］. IEEE Signal Processing Magazine, 2005, 22: 12-23.
［6］李阳铭,孟庆虎,梁华为,等.基于粒子滤波的无线传感器网络辅助同步定位与地图创建方法研究［J］.机器人, 2008: 30(5): 421-427.
 LI Yangming, MENG Qinghu, LIANG Huawei, et al. On WSN-aided simultaneous localization and mapping based on particle filtering［J］. Robot, 2008: 30(5): 421-427.
［7］MENEGATTI E, ZANELLA A, ZILLI S, et al. Range-only SLAM with a mobile robot and a wireless sensor networks［C］//Proceedings of the 2009 IEEE International Conference on Robotics and Automation. Kobe, Japan, 2009: 1699-1705.
［8］JENSFELT P, CHRISTENSEN H I. Laser based pose tracking［C］//Proceedings of the 1999 IEEE International Conference on Robotics and Automation. Detroit, USA, 1999: 2994-3000.
［9］CABALLERO F, MERINO L, GIL P, et al. A probabilistic frameworks for entire WSN localization using a mobile robot［J］. Robotics and Autonomous Systems, 2008, 56(10): 798-806.
［10］THRUN S, BURGARD W, FOX D. Probabilistic robotics［M］. Cambridge, USA: MIT Press, 2005.
［11］REKLEITIS I M. A particle filter tutorial for mobile robot localization, technical report TR-CIM-04-02［R］. Montreal, Canada: Centre for Intelligent Machines, McGill University, 2004.
［12］BLANCO J L, FEMANDEZMADRIGAL J A, GONZALEZ J. Efficient probabilistic range-only SLAM［C］// IEEE/RST International Conference on Intelligent Robots and Systems. Nice, France, 2008: 1017-1022.
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