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Building a cognitive map using an SOM2

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EN
Abstrakty
EN
In this paper, we propose a new method for building an environmental map in a self-organizing manner using visual information from a mobile robot. This method is based on a Higher Rank of Self-Organizing Map (SOM ), in which Kohonen’s SOM is extended to create a map of data distributions (set of manifolds). It is expected that the “SOM” is capable of creating an environmental map in a self-organizing manner from visual information, since the set of visual information obtained from each position in the environment forms a manifold at every position. We also show the effectiveness of the proposed method.
Twórcy
autor
autor
  • Department of Brain Science and Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu-ku, Kitakyushu 808- 0196, Japan, tokunaga@brain.kyutech.ac.jp
Bibliografia
  • [1] O’keefe J., Dostrovsky J., “The hippocampus as a spatial map: preliminary evidence from unit activity in the freely moving rat “, Brain. Res. , vol. 34, 1971, pp. 171175.
  • [2] O’keefe J. , Nadal L., The Hippocampus as a Cognitive Map, Oxford: Oxford. University Press, 1978.
  • [3] Taube J.S., Muller R.U., Ranck J.B. Jr., “Head-direction Cells Recorded from the Postsubiculum in Freely Moving Rats”, Journal of Neuroscience , vol. 10, 1990, pp. 436447.
  • [4] Furukawa T., “SOM of SOMs”, Neural Networks , vol. 22, issue 4, 2009, pp. 463-478.
  • [5] Kohone T., Self-Organizing Maps , Springer-Verlag: New York, 2001.
  • [6] Gamini Dissanayake M.W., Newman P., Clark S., Durrantwhyte H.F., Csorba M., “A solution to the simultaneous localization and map building (SLAM) problem”, IEEE Transactions on Robotics and Automation , vol. 17, 2001, pp. 229-241.
  • [7] Durrant-Whyte H.F., Bailey T., “Simultaneous Localisation and Mapping (SLAM): Part I The Essential Algorithms”, Robotics and Automation Magazine, vol. 13, 2006, pp. 99–110.
  • [8] Sim R., Elinas P., Griffin M., “Vision-based SLAM using the rao-blackwellised particle filter”.” In: IJCAI Workshop on Reasoning with Uncertainty in Robotics, 2005.
  • [9] Se S., Lowe D.G., Little J.J., “Vision-based global localization and mapping for mobile robots,” IEEE Transactions on Robotics, vol. 21, Issue 3, 2005, pp. 364375.
  • [10] Yairi T., “Map Building without Localization by Dimensionality Reduction Techniques”. In: The 24 th International Conference on Machine Learning (ICML-2007) ,2007.
  • [11] Trullier O., Wiener S.I., Berthoz A., Meyer J., Berthelot P.M., “Biologically-based Artificial Navigation Systems: Review and prospects”, Progress in Neurobiology , vol. 51, 1997, pp. 483-544.
  • [12] Martinetz T., Schulten K., “Topology Representing Networks”, Neural Networks , vol. 7, issue 3, 1994, pp.507522.
  • [13] Takahashi T., Tanaka T., Nishida K., Kurita T., “Self-organization of place cells and reward-based navigation for a mobile robot”. In: Proceedings of ICONIP’01 , 2001, pp. 1164-1169.
  • [14] Chokshi K., Wermter S., Panchev C., Burn K., “Image Invariant Robot Navigation Based on Self Organizing Neural Place Codes”, Lecture notes in computer science ,vol. 3575, 2005, pp. 88-106.
  • [15] Loww D.G., “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, vol. 60, no. 3, 2004, pp. 91-110.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ7-0011-0007
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