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Adaptive Segmentation of Color Image for Vision Navigation of Mobile Robots

  • Zeng-Shun Zhao
  • Zeng-Guang Hou
  • Min Tan
  • Yong-Qian Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

The self-localization problem is very important when the mobile robot has to move in autonomous way. Among techniques for self-localization, landmark-based approach is preferred for its simplicity and much less memory demanding for descriptions of robot surroundings. Door-plates are selected as visual landmarks. In this paper, we present an adaptive segmentation approach based on Principal Component Analysis (PCA) and scale-space filtering. To speed up the entire color segmentation and use the color information as a whole, PCA is implemented to project tristimulus R, G and B color space to the first principal component (1st PC) axis direction and scale-space filtering is used to get the centers of color classes. This method has been tested in the color segmentation of door-plate images captured by mobile robot CASIA-1. Experimental results are provided to demonstrate the effectiveness of this proposed method.

Keywords

Principal Component Analysis Mobile Robot Color Image Color Space Visual Landmark 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zeng-Shun Zhao
    • 1
  • Zeng-Guang Hou
    • 1
  • Min Tan
    • 1
  • Yong-Qian Zhang
    • 1
  1. 1.Key Laboratory of Complex Systems and Intelligence Science, Institute of AutomationChinese Academy of SciencesBeijingChina

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