Please wait a minute...
浙江大学学报(工学版)
J4  2014, Vol. 48 Issue (3): 414-422    DOI: 10.3785/j.issn.1008-973X.2014.03.006
计算机技术，无线电电子学     
不依赖里程计的机器人定位与地图构建
康轶非,宋永端,宋宇,闫德立
北京交通大学 电子信息工程学院, 北京 100044
Simultaneous localization and  mapping without relying on odometer
KANG Yi-fei, SONG Yong-duan, SONG Yu, YAN De-li
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
 全文: PDF(2275 KB)  
摘要：

为了解决缺少里程计情况下的移动机器人同时定位与地图构建（SLAM）问题,提出一种机器人运动状态估计模型.通过将该模型与FastSLAM框架相结合,在SLAM过程中实现对机器人位置、姿态及其运动状态（如速度）的估计.该算法用估计的运动状态代替里程计,实现了在没有里程计情况下的SLAM.为验证算法性能,通过仿真和维多利亚数据库的实验将该算法与需要里程计信息的SLAM算法相对比.实验结果表明,该算法在大于30个粒子的情况下可以达到与需要里程计信息的SLAM算法相当的精度.
关键词： SLAM;  粒子滤波;  机器人运动估计模型    
Abstract:

A model for estimating robot motion state was proposed to handle simultaneous localization and mapping (SLAM) without odometry. By combining this model with framework of FastSLAM, the proposed algorithm estimates the robot position,  pose and  motion state (such as speed) during SLAM. The proposed algorithm uses the estimated motion state instead of odometer, thus enables SLAM with noodometer. The performance of the algorithm  was verified by comparison of the proposed algorithm with the SLAM algorithm including odometry information by simulation and Victoria database. Experimental results show that the proposed algorithm can  achieve the same accuracy as that of the SLAM algorithm with odometer information in the case of the particles larger than 30．
Key words:  SLAM    particle filter    robot motion estimation model
出版日期: 2014-04-02
:  TP 242  
基金资助:

国家“973”重大专项规划资助项目(2012CB215202)；国家自然科学基金资助项目(61134001,60909055)；国家“863”高技术研究发展计划资助项目(SS2012AA052302)；中央高校基本科研业务费专项资金资助项目(2014JBM014).
通讯作者: 宋永端，男，教授.     E-mail: ydsong@bjtu.edu.cn
作者简介: 康轶非(1988-)，男，博士生，从事车辆定位的研究.E-mail:ccyclonel@gmail.com
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　

引用本文:

康轶非,宋永端,宋宇,闫德立. 不依赖里程计的机器人定位与地图构建[J]. J4, 2014, 48(3): 414-422.

KANG Yi-fei, SONG Yong-duan, SONG Yu, YAN De-li. Simultaneous localization and  mapping without relying on odometer. J4, 2014, 48(3): 414-422.

链接本文:

http://www.zjujournals.com/xueshu/eng/CN/10.3785/j.issn.1008-973X.2014.03.006    或    http://www.zjujournals.com/xueshu/eng/CN/Y2014/V48/I3/414

［1］ SMITH R, CHEESMAN P. On the representation and Estimation of Spatial Uncertainty［J］. IEEE Transactions on Robotics Res, 1987, 5(4): 56-68．
［2］ DelLAERT F, FOX D, BURGARD W, et al. Monte carlo localization for mobile robots［C］∥IEEE International Conference on Robotics and Automation (ICRA). Detroit, USA: IEEE, 1999: 1322-1328．
［3］ MONTEMERLO M, THRUN S, KOLLER D, et al. FastSLAM: A factored solution to the simultaneous localization and mapping problem［C］∥Proc. AAAI National Conference on Artificial Intelligence. Edmonton, Canada: AAAI, 2002: 593-598．
［4］ MONTEMERLO M, THRUN S, KOLLER D, et al. FastSLAM2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges［C］∥International Joint Conferences on Artificial Intelligence. Mexico：［s.n.］, 2003: 1151-1156．
［5］ SONG Y, LI Qing-ling, KANG Yi-fei, et al. CFastSLAM: a new Jacobian free solution to SLAM problem［C］∥ International Conference on Robotics and Automation (ICRA). Saint Paul, USA: IEEE, 2012: 3063-3068．
［6］ LU F, MILIOS E. Robot pose estimation in unknown environments by matching 2D range scans［C］∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Seattle: IEEE,1994: 935-938．
［7］ COX I. Blanche an experiment in guidance and navigation of an autonomous robot vehicle［J］. IEEE Transactions on Robotics and Automation, 1991,7(2): 193-204．
［8］ GUTMANN J, KONOLIGE K. Incremental mapping of large cyclic environments［C］∥IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA).California: IEEE, 1999: 318-325．
［9］ LINGERMANN K, SURMANN H, NUCHTER A, et al. Indoor and outdoor localization for fast mobile robots［C］∥ IEEE International Conference on Intelligent Robots and Systems. Sendai, Japan: IEEE, 2004: 2185-2190．
［10］ BOSSE M C. ATLAS: A Framework for Large Scale Automated Mapping and Localization ［D］. Boston：Massachusetts Institute of Technology, 2004．
［11］ BOSSE M C, NEWMAN P, LEONARD J, et al. An atlas framework for scalable mapping［C］∥ IEEE International Conference on Robotics and Automation. Taipei, Taiwan: IEEE, 2003: 1899-1906．
［12］ BOSSE M C, NEWMAN P, LEONARD J, et al. Simultaneous localization and map building in large-scale cyclic environments using the atlas framework［J］.International Journal of Robotics Research, 2004, 23(12): 1113-1139．
［13］ ZEZHONG X, JILIN L, ZHIYU X. Scan matching based on CLS relationships［C］∥IEEE International Conference on Robotics, Intelligent Systems and Signal Processing. Changsha, China: IEEE,2003: 99-104．
［14］ WEISS G, WETZLER C, VON E PUTTKAMER. Keeping track of position and orientation of moving indoor systems by correlation of range-nder scans［C］∥ IEEE International Conference on Intelligent Robots and Systems. Munich, Germany: IEEE,1994: 595-601．
［15］ RFER T. Using histogram correlation to create consistent laser scan maps［C］∥IEEE International Conference on Intelligent Robots and Systems. Lausanne, Switzerland: IEEE, 2002: 625-630．
［16］ GUTMANN J S, WEIGEL T, NEBEL B. A fast, accurate, and robust method for self-localization in polygonal environments using laser-range-nders［J］. Journal of Advanced Robotics, 2001, 14(8): 651-668．
［17］ PAULL L, SAEEDI S, SETO M, et al. AUV navigation and localization: a review［J］. IEEE Journal of Oceanic Engineering, 2014,39(1) :131-149．
［18］ GRASA O G, BERNAL E, CASADO S, et al. Visual SLAM for handheld monocular endoscope［J］. IEEE Transactions on Medical Imaging, 2014, 33(1):135-146．
［19］ MURPHY K. Bayesian map learning in dynamic environments ［M］. Boston, MIT Press, 1999．
［20］ SONG Yu, LI Qing-ling. Visual tracking based on multiple instance learning particle filter［C］∥ International Conference on Mechatronics and Automation (ICMA).Beijing, China: IEEE, 2011: 1063-1067．
［21］ 宋宇,孙富春,李庆玲. 移动机器人的改进无迹粒子滤波蒙特卡罗定位算法［J］.自动化学报, 2010, 36(6): 851-857．
SONG Yu, SUN Fu-chun, LI Qing-ling. Mobile robot Monte Carlo Localization based on improved unscented particle filter［J］. The Journal of Acta Automatica Sinica, 2010, 36(6): 851-857.
［22］ MOURAD F, HONEINE P, SNOUSSI H. Indoor localization using polar intervals in wireless sensor networks［C］∥ International Conference on Telecom munications (ICT). Jounieh, Lebanon: IEEE, 2012: 16．
［23］ DURRANT-WHYTE H, BAILEY T. Simultaneous Localization and Mapping: Part I［J］. IEEE Robotics and Automation Magazine, 2006, 13(2): 99-110．
[1] 蒋鑫龙, 陈益强, 刘军发, 忽丽莎, 沈建飞. 面向自闭症患者社交距离认知的可穿戴系统[J]. 浙江大学学报(工学版), 2017, 51(4): 637-647.
[2] 陈家乾, 何衍, 蒋静坪. 基于权值平滑的改良FastSLAM算法[J]. J4, 2010, 44(8): 1454-1459.
[3] 谷雨, 李平, 韩波. 基于分层粒子滤波的地标检测与跟踪[J]. J4, 2010, 44(4): 687-691.
[4] 许士芳 谢立 刘济林. 基于MCMC粒子滤波的机器人定位[J]. J4, 2007, 41(7): 1083-1087.
[5] 武二永 项志宇 沈敏一 刘济林. 大规模环境下基于激光雷达的机器人SLAM算法[J]. J4, 2007, 41(12): 1982-1986.
[6] 洪涛 王申康. 基于图像特征分析的人体正面运动跟踪研究[J]. J4, 2005, 39(6): 845-848.