Please wait a minute...
J4  2014, Vol. 48 Issue (3): 414-422    DOI: 10.3785/j.issn.1008-973X.2014.03.006
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)   HTML



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.

出版日期: 2018-06-10
:  TP 242  


通讯作者: 宋永端,男,教授.     E-mail:
作者简介: 康轶非(1988-),男,博士生,从事车辆定位的研究
E-mail Alert


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.


[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] 陈伟海, 陈泉柱, 刘荣, 张建斌, 崔翔. 绳驱动拟人臂机器人回零算法分析[J]. J4, 2013, 47(2): 345-352.
[2] 金波, 陈诚,李伟. 基于能耗优化的六足步行机器人力矩分配[J]. J4, 2012, 46(7): 1168-1174.
[3] 彭铁柱,李凌丰. 无奇异3UPS+1RPU新型并联机构[J]. J4, 2010, 44(11): 2056-2062.