Growing Neural Gas based Traversability Clustering for an Autonomous Robot

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

1 Citation (Scopus)

Abstract

One of the most important capabilities of an autonomous robot is to recognize a 3D space of the surrounding environment in real-time from a 3D point cloud measured by a 3D distance sensor. The area in which the robot can travel is limited by a robot embodiment such as a mechanism of the robot. Therefore, the traversability estimation method helps the robot to travel safely and reduces the calculation cost of the path planning. This paper proposes Growing Neural Gas (GNG) based traversability estimation method by utilizing a topological structure learned from the 3D point cloud data. However, the conventional GNG cannot preserve the geometric information of the 3D point cloud if the input vector is composed of the multiple properties. Therefore, this paper apply GNG with Different Topologies (GNG-DT) that learn the multiple topological structures according to the number of properties. This paper proposes a GNG-DT based traversability estimation method by redefining the property of the GNG-DT. We conduct several experiments in both simulation and real environment to verify the effectiveness of our proposed method.

Original languageEnglish
Title of host publicationIJCNN 2023 - International Joint Conference on Neural Networks, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665488679
DOIs
Publication statusPublished - 2023
Event2023 International Joint Conference on Neural Networks, IJCNN 2023 - Gold Coast, Australia
Duration: Jun 18 2023Jun 23 2023

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2023-June

Conference

Conference2023 International Joint Conference on Neural Networks, IJCNN 2023
Country/TerritoryAustralia
CityGold Coast
Period6/18/236/23/23

Keywords

  • Growing Neural Gas
  • Traversability estimation
  • Unsupervised learning

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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