Mobile robot vision-based navigation using self-organizing and incremental neural networks

Authors:
Sirinart Tangruamsub;Manabu Tsuboyama;Aram Kawewong;Osamu Hasegawa
Affiliations:
Imaging science and Engineering Laboratory, Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan;Imaging science and Engineering Laboratory, Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan;Imaging science and Engineering Laboratory, Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan;Imaging science and Engineering Laboratory, Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 4
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Abstract

A new approach for vision-based navigation in mobile robots is presented. Instead of incremental spectral clustering (ISC), which is considered state-of-the-art, the method of self-organizing incremental neural networks (SOINN) is used for visual space clustering. Using SOINN, the number of nodes in the topological map matches well with the environment. The time used for incremental map building is markedly less than that used for ISC. Furthermore, the rate of single image classification is higher than that of ISC.