Semantic segmentation of 3D point cloud data acquired from robot dog for scaffold monitoring

Juhyeon Kim, Duho Chung, Yohan Kim, Hyoungkwan Kim

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

Abstract

Many of the fatalities and injuries in the construction industry occur in scaffolding accidents, and monitoring the scaffolding process and checking compliance are critical. However, monitoring scaffolds is labor-intensive and inefficient because it is done manually. To address this issue, we propose an advanced 3D reconstruction method for detecting and monitoring scaffolds. Deep learning-based RandLA-Net architecture is used to perform scene segmentation. RandLA-Net is trained based on transfer learning, using the knowledge of the model learned with the Semantic3D dataset. RandLA-Net uses 3D point cloud data that are matched and registered by LIO-SAM, a laser slam algorithm. By attaching a LiDAR to a quadruped robot, it is possible to obtain data frequently in a manner suitable for construction sites. The proposed methodology has demonstrated good performance in monitoring scaffolds.

Original languageEnglish
Title of host publicationProceedings of the 38th International Symposium on Automation and Robotics in Construction, ISARC 2021
EditorsChen Feng, Thomas Linner, Ioannis Brilakis
PublisherInternational Association for Automation and Robotics in Construction (IAARC)
Pages784-788
Number of pages5
ISBN (Electronic)9789526952413
Publication statusPublished - 2021
Event38th International Symposium on Automation and Robotics in Construction, ISARC 2021 - Dubai, United Arab Emirates
Duration: 2021 Nov 22021 Nov 4

Publication series

NameProceedings of the International Symposium on Automation and Robotics in Construction
Volume2021-November
ISSN (Electronic)2413-5844

Conference

Conference38th International Symposium on Automation and Robotics in Construction, ISARC 2021
Country/TerritoryUnited Arab Emirates
CityDubai
Period21/11/221/11/4

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (No. 2021R1A2C2004308) and the "National R&D Project for Smart Construction Technology (No.21SMIP-A158708-02)" funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport, and managed by the Korea Expressway Corporation.

Publisher Copyright:
© 2021 Proceedings of the International Symposium on Automation and Robotics in Construction. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Building and Construction
  • Civil and Structural Engineering
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality
  • Artificial Intelligence
  • Computer Science Applications

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