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Artificial Life and Robotics
Published in: Artificial Life and Robotics 2/2017

30-01-2017 | Original Article

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

Authors: Maierdan Maimaitimin, Keigo Watanabe, Shoichi Maeyama

Published in: Artificial Life and Robotics | Issue 2/2017

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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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Metadata
Title
Stacked convolutional auto-encoders for surface recognition based on 3d point cloud data
Authors
Maierdan Maimaitimin
Keigo Watanabe
Shoichi Maeyama
Publication date
30-01-2017
Publisher
Springer Japan
Published in
Artificial Life and Robotics / Issue 2/2017
Print ISSN: 1433-5298
Electronic ISSN: 1614-7456
DOI
https://doi.org/10.1007/s10015-017-0350-9

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