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ID 60825
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Author
Li, Xiang Okayama University
Matsuno, Takayuki Okayama University
Minami, Mamoru Okayama University
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.
Keywords
Growing Neural Gas
Point cloud processing
Topological structure learning
Note
International Conference on Intelligent Robotics and Applications (ICIRA) 2019 Lecture Notes in Computer Science, vol 11742
Published Date
2019-08-02
Publication Title
Intelligent Robotics and Applications
Publisher
Springer
Start Page
82
End Page
91
Content Type
Conference Paper
language
英語
OAI-PMH Set
岡山大学
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DOI
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isVersionOf https://doi.org/10.1007/978-3-030-27535-8_8