期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

自主移动机器人室内定位方法研究综述 被引量：32

Research overview of indoor localization methods for autonomous mobile robots
下载PDF
导出
摘要 自主移动机器人的室内定位作为机器人研究领域中最基本的问题已被广泛研究。根据定位技术和传感器的不同,将室内定位方法分为航迹推算定位、地图匹配定位和基于信标定位三类。详细介绍了超声波网络定位系统和基于无线射频识别(RFID)的定位方法。对几种基于概率的定位算法做了分析和对比,并对自主移动机器人室内定位方法的研究方向做了展望。 Being a fundamental issue in the research of issue,indoor localization of autonomous mobile robots has been thoroughly studied. According to the difference between localization techniques and sensors indoor localization methods are classified into three categories：dead-reckoning localization, map-matching localization and beacon-based localization. The ultrasonic wave network positioning system and RFID-based localization method are interpreted in detail. Several localization algorithms based on probability are analyzed and compared, the research directions of autonomous mobile robot localization methods are prospected.
作者 高云峰 周伦 吕明睿 刘文涛
机构地区 哈尔滨工业大学机器人技术与系统国家重点实验室
出处 《传感器与微系统》 CSCD 北大核心 2013年第12期1-5,9,共6页 Transducer and Microsystem Technologies
基金 国家科技支撑计划资助项目(2012BAI33B04) 机器人技术与系统国家重点实验室自主课题项目(SKLRS201201B)
关键词 自主移动机器人 室内定位方法 航迹推算定位 地图匹配定位 信标定位 概率算法 autonomous mobile robot indoor localization methods dead-reckoning localization map-matching localization beacon-based localization probability-based algorithm
分类号 TP242 [自动化与计算机技术—检测技术与自动化装置]
  • 相关文献

参考文献35

  • 1Barshan B, Durrant-Whyte H F. Inertial navigation systems for mobile robots [ J]. IEEE Transactions on Robotics and Automa- tion,1995,11 (3) :328 -342.
  • 2Borenstein J, Feng L. Measurement and correction of systematic odometry errors in mobile robots [ J ]. IEEE Transactions on Ro- botics and Automation, 1996,12 ( 6 ) :869 -880.
  • 3Ibraheem M. Gyroscope-enhanced dead reckoning localization sys- tem for an intelligent walker[ C ]/International Conference on In- formation ,Networking and Automation(ICINA) ,2010:67-72.
  • 4Cbo B,Moon W,Seo W, et al. A dead reckoning localization sys- tem for mobile robots using inertial sensors and wheel revolution encoding [ J ]. Journal of Mechanical Science and Technology, 2011,25 ( 11 ) :2907 -2917.
  • 5Konolige K, Marder-Eppstein E, Marthi B, et al. Navigation in hy- brid metric-topological maps [ C]//2011 IEEE International Con- ference on Robotics and Automation, Shanghai, China, 2011: 3041 -3047.
  • 6Ozkil A, Fan Z, Xiao J, et al. Practical indoor mobile robot naviga- tion using hybrid maps [ C ]//Proceedings of the 2011 IEEE Inter- national Conference on Mechatronics, Istanbul, Turkey, 2011: 475 -480.
  • 7Lee S, Lim J, Cho D. General feature extraction for mapping and lo- calization of a mobile robot using sparsely sampled sonar data[J]. Advanced Robotics ,2009,23 : 1601 -1616.
  • 8Choi J, Choi M, Chung W, et al. Topological localization with kid- nap recovery using sonar grid map matching in a home environ- ment [ J ]. Robotics and Computer-Integrated Manufacturing, 2012,28 (3) :366 -374.
  • 9Liu Y, Sun Y. Mobile robot instant indoor map building and local- ization using 2D laser scanning data [ C ]//International Confer- ence on System Science and Engineering, Dalian, China,2012: 339 -344.
  • 10Hornung A, Wurm K, Bennewitz M. Humanoid robot localization in complex indoor environments [ C ]//The 2010 IEEE/RSJ Inter- national Conference on Intelligent Robots and Systems, Taipei, Taiwan ,2010 : 1690 -1695.

二级参考文献17

  • 1方正,佟国峰,徐心和.一种鲁棒高效的移动机器人定位方法[J].自动化学报,2007,33(1):48-53. 被引量：15
  • 2Sanjeev A, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/nomGaussian Bayesian tracking. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188.
  • 3Dellaertt F, Fox D, Burgaxd W, Thrun S. Monte Carlo localization for mobile robots. In: Proceedings of IEEE Inter- national Conference on Robotics and Automation. Detroit, USA: IEEE, 1999. 1322-1328.
  • 4Thrun S, Fox D, Burgard W, Dellmert F. Robust Monte Carlo localization for mobile robots. Artificial Intelligence, 2001, 128(1-2): 99-141.
  • 5Fox D. Adapting the sample size in particle filters through KLD-sampling. International Journal of Robotics Research, 2003, 22(12): 985-1103.
  • 6Lenser S, Veloso M. Sensor resetting localization for poorly modeled mobile robots. In: Proceedings of IEEE International Conference on Robotics and Automation. San Francisco, USA: IEEE, 2000. 1225-1232.
  • 7Ueda R, Arai T, Sakamoto K, Kikuchi T, Kamiya S. Expansion resetting for recovery from fatal error in Monte Carlo localization-comparison with sensor resetting methods. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. Sendai, Japan: IEEE, 2004. 2481-2486.
  • 8Khan Z, Balch T, Dellaert F. MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(11): 1805-1918.
  • 9van der Merwe R, de Freitas N, Doucet A, Wan E. The Unscented Particle Filter, Technical Report CUED/FINFENG/TR 380, Department of Engineering, Cambridge University, UK, 2000.
  • 10Wu H, Sun F C, Liu H P. Fuzzy particle filtering for uncertain systems. IEEE Transactions on Fuzzy Systems, 2008, 16(5): 1114-1129.

共引文献27

同被引文献233

引证文献32

二级引证文献134

传感器与微系统
2013年 第12期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部