Dynamic Density Topological Structure Generation for Real-Time Ladder Affordance Detection

Azhar Aulia Saputra, Wei Hong Chin, Yuichiro Toda, Naoyuki Takesue, Naoyuki Kubota

研究成果

11 被引用数 (Scopus)

抄録

This paper presents a method with dynamic density topological structure generation for low-cost real-time vertical ladder detection from 3D point cloud data. Dynamic Density Growing Neural Gas (DD-GNG) is proposed to generate a dynamic density of the topological structure. The density of the structure and the number of nodes will be increased in the targeted object area. Feature extraction model is required to classify suspected objects for being processed in the next time process. After that, rungs of the vertical ladder is processed using an inlier-outlier method. Thus, the ladder detection model represents the ladder with a set of nodes and edges. Next, affordance detection is processed for detecting the feasible grasped location. To validate the effectiveness of the proposed method, a series of experiments are conducted on a 4-legged robot with a non-GPU board for real-time vertical ladder detection and climbing to validate the effectiveness of the proposed method. Results show that our proposed method able to detect and track the ladder structure in real-time with a much lower computational cost. The affordance of the ladder provides safety information for robot grasping.

本文言語English
ホスト出版物のタイトル2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3439-3444
ページ数6
ISBN(電子版)9781728140049
DOI
出版ステータスPublished - 11月 2019
外部発表はい
イベント2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 - Macau
継続期間: 11月 3 201911月 8 2019

出版物シリーズ

名前IEEE International Conference on Intelligent Robots and Systems
ISSN(印刷版)2153-0858
ISSN(電子版)2153-0866

Conference

Conference2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
国/地域China
CityMacau
Period11/3/1911/8/19

ASJC Scopus subject areas

  • 制御およびシステム工学
  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ サイエンスの応用

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