ID | 60825 |
FullText URL | |
Author |
Li, Xiang
Okayama University
Matsuno, Takayuki
Okayama University
Minami, Mamoru
Okayama University
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Abstract | This paper proposes a real-time topological structure learning method based on concentrated/distributed sensing for a 2D/3D point cloud. First of all, we explain a modified Growing Neural Gas with Utility (GNG-U2) that can learn the topological structure of 3D space environment and color information simultaneously by using a weight vector. Next, we propose a Region Of Interest Growing Neural Gas (ROI-GNG) for realizing concentrated/distributed sensing in real-time. In ROI-GNG, the discount rates of the accumulated error and utility value are variable according to the situation. We show experimental results of the proposed method and discuss the effectiveness of the proposed method.
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Keywords | Growing Neural Gas
Point cloud processing
Topological structure learning
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Note | International Conference on Intelligent Robotics and Applications (ICIRA) 2019
Lecture Notes in Computer Science, vol 11742
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Published Date | 2019-08-02
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Publication Title |
Intelligent Robotics and Applications
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Publisher | Springer
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Start Page | 82
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End Page | 91
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Content Type |
Conference Paper
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language |
English
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OAI-PMH Set |
岡山大学
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File Version | author
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DOI | |
Web of Science KeyUT | |
Related Url | isVersionOf https://doi.org/10.1007/978-3-030-27535-8_8
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