Region of interest growing neural gas for real-time point cloud processing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationIntelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings
EditorsZhaojie Ju, Dalin Zhou, Haibin Yu, Jinguo Liu, Lianqing Liu, Yuwang Liu
PublisherSpringer Verlag
Pages82-91
Number of pages10
ISBN (Print)9783030275341
DOIs
Publication statusPublished - Jan 1 2019
Event12th International Conference on Intelligent Robotics and Applications, ICIRA 2019 - Shenyang, China
Duration: Aug 8 2019Aug 11 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11742 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Intelligent Robotics and Applications, ICIRA 2019
CountryChina
CityShenyang
Period8/8/198/11/19

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Keywords

  • Growing Neural Gas
  • Point cloud processing
  • Topological structure learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Toda, Y., Li, X., Matsuno, T., & Minami, M. (2019). Region of interest growing neural gas for real-time point cloud processing. In Z. Ju, D. Zhou, H. Yu, J. Liu, L. Liu, & Y. Liu (Eds.), Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings (pp. 82-91). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11742 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-27535-8_8