Intensity histogram based segmentation of 3D point cloud using Growing Neural Gas

Shin Miyake, Yuichiro Toda, Naoyuki Kubota, Naoyuki Takesue, Kazuyoshi Wada

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

2 Citations (Scopus)

Abstract

This paper proposes a 3D point cloud segmentation method using a reflection intensity of Laser Range Finder (LRF). In this paper, we use LRF and tilt unit for acquiring a 3D point cloud. First of all, we apply Growing Neural Gas (GNG) to the point cloud for learning a topological structure of the point cloud. Next, we proposed a segmentation method based on an intensity histogram that is composed of the nearest data of each node. Finally, 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 - 9th International Conference, ICIRA 2016, Proceedings
PublisherSpringer Verlag
Pages335-345
Number of pages11
ISBN (Print)9783319435176
DOIs
Publication statusPublished - Jan 1 2016
Externally publishedYes
Event9th International Conference on Intelligent Robotics and Applications, ICIRA 2016 - Tokyo, Japan
Duration: Aug 22 2016Aug 24 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9835 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Intelligent Robotics and Applications, ICIRA 2016
CountryJapan
CityTokyo
Period8/22/168/24/16

Keywords

  • Clustering
  • LRF intensity
  • Robot sensing

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Miyake, S., Toda, Y., Kubota, N., Takesue, N., & Wada, K. (2016). Intensity histogram based segmentation of 3D point cloud using Growing Neural Gas. In Intelligent Robotics and Applications - 9th International Conference, ICIRA 2016, Proceedings (pp. 335-345). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9835 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-43518-3_33