Adaptive Segmentation of Color Image for Vision Navigation of Mobile Robots
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.
KeywordsPrincipal Component Analysis Mobile Robot Color Image Color Space Visual Landmark
Unable to display preview. Download preview PDF.
- 1.Mata, M., Armingol, J.M., de la Escalera, A., Salichs, M.A.: A Visual Landmark Recognition System for Topological Navigation of Mobile Robots. In: Proceedings of IEEE International Conference on Robotics and Automation, vol. 2, pp. 1124–1129 (2001)Google Scholar
- 2.Yoon, K.J., Kweon, I.S.: Artificial Landmark Tracking Based on the Color Histogram. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1918–1923 (2001)Google Scholar
- 3.Moon, I., Miura, J., Shirai, Y.: Automatic Extraction of Visual Landmarks for a Mobile Robot Under Uncertainty of Vision and Motion. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 1188–1193 (2001)Google Scholar
- 4.Balkenius, C.: Spatial Learning with Perceptually Grounded Representations. In: Proceedings of The Second Euromicro Workshop on Advanced Mobile Robots, pp. 16–21 (1997)Google Scholar
- 6.Gonzale, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Upper Saddle River (2002)Google Scholar
- 8.Diamantras, K.I., Kung, S.Y.: Principal Component Neural Networks: Theory and Applications. John Wiley and Sons, New York (1996)Google Scholar
- 10.Witkin, A.P.: Scale-Space Filtering. In: Proceedings of International Joint Conference on Artificial Intelligence, Karlsruhe, West Germany, pp. 1019–1021 (1983)Google Scholar
- 12.Dehghan, H.: Zero-Crossing Contour Construction for Scale-Space Filtering. In: Conference Record of the Thirty-First Asilomar Signals, Systems and Computers, vol. 2, pp. 1479–1483 (1997)Google Scholar
- 13.An, C. W., Li, G.Z.H., Zhang, Y.Q., Tan, M.: Doorplate Adaptive Detection and Recognition for Indoor Mobile Robot Self-Localization. In: Proceedings of IEEE International Conference on Robotics and Biomimetics, pp. 339–343 (2004)Google Scholar