A Multi-Granularity Scene Segmentation Network for Human-Robot Collaboration Environment Perception

Junming Fan, Pai Zheng (Corresponding Author), Carman K.M. Lee

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Human-robot collaboration (HRC) has been considered as a promising paradigm towards futuristic human-centric smart manufacturing, to meet the thriving needs of mass personalization. In this context, existing robotic systems normally adopt a single-granularity semantic segmentation scheme for environment perception, which lacks the flexibility to be implemented to various HRC situations. To fill the gap, this study proposes a multi-granularity scene segmentation network. Inspired by some recent network designs, we construct an encoder network with two ConvNext-T backbones for RGB and depth respectively, and an decoder network consisting of multi-scale supervision and multi-granularity segmentation branches. The proposed model is demonstrated in a human-robot collaborative battery disassembly scenario and further evaluated in comparison with state-of-the-art RGB-D semantic segmentation methods on the NYU-Depth V2 dataset.

Original languageEnglish
Title of host publicationIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2105-2110
Number of pages6
ISBN (Electronic)9781665479271
DOIs
Publication statusPublished - Oct 2022
Event2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022 - Kyoto, Japan
Duration: 23 Oct 202227 Oct 2022

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
Volume2022-October
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
Country/TerritoryJapan
CityKyoto
Period23/10/2227/10/22

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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