基于场景识别的移动机器人定位方法研究

李桂芝, 安成万, 杨国胜, 谭民, 涂序彦

李桂芝, 安成万, 杨国胜, 谭民, 涂序彦. 基于场景识别的移动机器人定位方法研究[J]. 机器人, 2005, 27(2): 123-127.
引用本文: 李桂芝, 安成万, 杨国胜, 谭民, 涂序彦. 基于场景识别的移动机器人定位方法研究[J]. 机器人, 2005, 27(2): 123-127.
LI Gui-zhi, AN Cheng-wan, YANG Guo-sheng, TAN Min, TU Xu-yan. Scene Recognition for Mobile Robot Localization[J]. ROBOT, 2005, 27(2): 123-127.
Citation: LI Gui-zhi, AN Cheng-wan, YANG Guo-sheng, TAN Min, TU Xu-yan. Scene Recognition for Mobile Robot Localization[J]. ROBOT, 2005, 27(2): 123-127.

基于场景识别的移动机器人定位方法研究

详细信息
    作者简介:

    李桂芝(1971- ),女,博士研究生.研究领域:机器人视觉,多传感器数据融合.
    安成万(1974- ),男,博士研究生.研究领域:图像处理,机器人视觉,多传感器数据融合.
    杨国胜(1963- ),男,副教授,博士后.研究领域:多传感器多目标数据融合技术,复杂系统与智能控制等.

  • 中图分类号: TP24

Scene Recognition for Mobile Robot Localization

  • 摘要: 提出了一种基于场景识别的移动机器人定位方法.对CCD采集的工作环境的系列场景图像,用多通道Gabor滤波器提取场景图像的全局纹理特征,然后通过SVM分类器来识别场景图像,实现机器人的逻辑定位.在移动机器人CASIAI上对该算法进行了实验.实验结果表明,该定位方法可达到91.11%的定位准确率,对光照、对比度等因素有较强的鲁棒性,并且满足机器人实时定位的要求.
    Abstract: This paper proposes a scene recognition approach for mobile robot localization.The multi-channel Gabor filters are used to extract the global texture features of the scene images which are associated with the corresponding locations,and then these texture features are fed back to support the vector machine classifier to determine the logical location of the robot.The algorithm has been tested on the autonomous mobile robot CASIA-I designed and developed by us.The experiment results indicate that the algorithm can reach up to a correct localiztion rate of 91.11%,is robust to the various illumination and contrast,and satisfies the real-time localization demand of the mobile robot.
  • [1] Ulrich I,Nourbakhsh I. Appearance-based place recognition for topological localization [A]. Proceedings of the IEEE International Conference on Robotics and Automation[C]. 2000.1023-1029.
    [2] Zhou C,Wei Y C,Tan T N. Mobile robot self-localization based on global visual appearance feature[A]. Proceedings of the 2003 IEEE International Conference on Robotics and Automation[C]. 2003.1271-1276.
    [3] Carreira M J,Orwell J,Turnes R,et al. Perceptual grouping from Gabor filter responses[A].Proceedings of the Ninth British Machine Vision Conference[C]. Southampton,UK: 1998. 336-345.
    [4] Manjunath B,Ma W Y. Textures features for browsing and retrieval of image data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(8): 837-842.
    [5] Lee T S. Image representation using 2D gabor wavelets[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(10): 959-971.
    [6] Rui Y,Huang T S,Chang S F. Image retrieval: past,present,and future[J]. Journal of Visual Communication and Image Representation,1999,10(1):1-23.
    [7] Vapnik V N. Statistical Learning Theory[M].Wiley,New York:1998.
    [8] Cristianini N,Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-based Methods[M]. Cambridge,UK: Cambridge University Press,2000.
    [9] Bovik A C,Clark M,Geisler W B. Multichannel texture analysis using localized spatial filters[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(1): 55-73.
    [10] Tan T N. Texture feature extraction via cortical channel modeling[A]. Proceedings of the 11th IAPR International Conference on Pattern Recognition[C]. 1992,3. 607-610.
    [11] Motton L,Cortes C,et al. Comparison of classifier methods: a case study in handwriting digit recognition[A]. Proceedings of the IEEE International Conference on Pattern Recognition[C]. 1994.77-87.
    [12] KreBel U. Pairwise Classification and Support Vector Machines[M]. Cambridge,MA: MIT Press,1999. 255-266.
    [13] Hsu C W,Lin C J.A comparison of methods for multi-class support vector machines[J]. IEEE Transactions on Neural Networks,2002,13(2):415-425.
    [14] Boser B E,Guyon I M,Vapnik V N.A training algorithm for optimal margin classifiers[A]. Proceedings of the Fifth Annual Workshop on Computational Learning Theory[C]. Pittsburgh,PA,USA: 1992. 144-152.
    [15] Osuna E,Freund R,Girosi F. An improved training algorithm for support vector machines[A]. Proceedings of the 1997 IEEE Workshop on Neural Networks for Signal Processing[C]. New York: IEEE Press,1997.276-285.
    [16] Platt J C. Fast training of Support Vector Machines Using Sequential Minimal Optimization [M]. Cambridge,MA: MIT Press,1999. 185-208.
计量
  • 文章访问数:  39
  • HTML全文浏览量:  1589
  • PDF下载量:  1233
  • 被引次数: 0
出版历程
  • 收稿日期:  2004-07-05

目录

    /

    返回文章
    返回
    x 关闭 永久关闭