국제학술발표논문집 (International conference on construction engineering and project management)

한국건설관리학회 (Korea Institute of Construction Engineering and Management)
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Real-time Construction Progress Monitoring Framework leveraging Semantic SLAM

  • Wei Yi HSU (Department of Civil Engineering, National Taiwan University) ;
  • Aritra PAL (Department of Civil Engineering, National Taiwan University) ;
  • Jacob J. LIN (Department of Civil Engineering, National Taiwan University) ;
  • Shang-Hsien HSIEH (Department of Civil Engineering, National Taiwan University)
  • 발행 : 2024.07.29
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초록

The imperative for real-time automatic construction progress monitoring (ACPM) to avert project delays is widely acknowledged in construction project management. Current ACPM methodologies, however, face a challenge as they rely on collecting data from construction sites and processing it offline for progress analysis. This delayed approach poses a risk of late identification of critical construction issues, potentially leading to rework and subsequent project delays. This research introduces a real-time construction progress monitoring framework that integrates cutting-edge semantic Simultaneous Localization and Mapping (SLAM) techniques. The innovation lies in the framework's ability to promptly identify structural components during site inspections conducted through a robotic system. Incorporating deep learning models, specifically those employing semantic segmentation, enables the system to swiftly acquire and process real-time data, identifying specific structural components and their respective locations. Furthermore, by seamlessly integrating with Building Information Modeling (BIM), the system can effectively evaluate and compare the progress status of each structural component. This holistic approach offers an efficient and practical real-time progress monitoring solution for construction projects, ensuring timely issue identification and mitigating the risk of project delays.

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과제정보

The support of the National Science and Technology Council (NSTC), Taiwan, is gratefully acknowledged. This work was supported by the NSTC under Grant No. 112-2221-E-002 -127 -MY3.

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