• DocumentCode
    2362828
  • Title

    Visual based Localization for mobile robots with Support Vector Machines

  • Author

    Shen, Jiali ; Hu, Huosheng

  • Author_Institution
    Dept. of Comput. Sci., Essex Univ., Colchester
  • fYear
    2006
  • fDate
    6-10 Nov. 2006
  • Firstpage
    4176
  • Lastpage
    4181
  • Abstract
    Support vector machine (SVM) is a relative new classification algorithm with some advantages over other machine learning methods. This paper presents a SVM based localisation algorithm for indoor robot navigation by recognizing the environmental features that are known as priori. A topological map is adopted by the mobile robot for the coarse global localisation. Then at each topological node located, geometrical grids are adopted for the proposed algorithm to provide fine position information of the mobile robot. Experimental results are presented to show the feasibility and good performance of the proposed method
  • Keywords
    feature extraction; mobile robots; navigation; support vector machines; coarse global localisation; environmental features recognition; fine position information; indoor robot navigation; machine learning methods; mobile robots; support vector machines; visual based localization; Computational efficiency; Histograms; Image edge detection; Machine learning algorithms; Mobile robots; Navigation; Robot kinematics; Simultaneous localization and mapping; Support vector machine classification; Support vector machines; Landmarks; Mobile robot; SLAM; Support Vector Machine; Visual based location;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    IEEE Industrial Electronics, IECON 2006 - 32nd Annual Conference on
  • Conference_Location
    Paris
  • ISSN
    1553-572X
  • Print_ISBN
    1-4244-0390-1
  • Type

    conf

  • DOI
    10.1109/IECON.2006.347454
  • Filename
    4152950