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Stacked convolutional auto-encoders for surface recognition based on 3d point cloud data

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Abstract

This paper addresses the problem of feature extraction for 3d point cloud data using a deep-structured auto-encoder. As one of the most focused research areas in human–robot interaction (HRI), the vision-based object recognition is very important. To recognize object using the most common geometry feature, surface condition that can be obtained from 3d point cloud data could decrease the error during the HRI. In this research, the surface normal vectors are used to convert 3D point cloud data to a surface-condition-feature map, and a sub-route stacked convolution auto-encoder (sCAE) is designed to classify the difference between the surfaces. The result of the trained filters and the classification of sCAE shows the surface-condition-feature and the specified sCAE are very effective in the variation of surface condition.

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Authors and Affiliations

  1. Division of Industrial Innovation Sciences, Department of Intelligent Mechanical Systems, Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama, 700-8530, Japan

    Maierdan Maimaitimin, Keigo Watanabe & Shoichi Maeyama

Authors
  1. Maierdan Maimaitimin
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  2. Keigo Watanabe
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  3. Shoichi Maeyama
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Corresponding author

Correspondence to Maierdan Maimaitimin.

Additional information

This work was presented in part at the 21st International Symposium on Artificial Life and Robotics, Beppu, Oita, January 20–22, 2016.

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Maimaitimin, M., Watanabe, K. & Maeyama, S. Stacked convolutional auto-encoders for surface recognition based on 3d point cloud data. Artif Life Robotics 22, 259–264 (2017). https://doi.org/10.1007/s10015-017-0350-9

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  • DOI: https://doi.org/10.1007/s10015-017-0350-9

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