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IoT - An Opportunity for Autonomous BIM Reconstruction?

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Advanced Computing Strategies for Engineering (EG-ICE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10863))

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Abstract

While the concepts and technologies of Building Information Modeling (BIM) are on their way to become an industry standard, there is an increasing demand for the digitalization of the existing building stock. Based on our previous work on this subject we present a framework for autonomous BIM reconstruction using an IoT-based approach. We show that it is possible to integrated depth sensing technologies with common devices that move regularly throughout a building. By autonomously conducting several scans of the same region it is possible to enhance the quality of the reconstructed model while reducing the human workload. The use of (semi-)autonomous devices however comes with a trade-off and in this study could not achieve results equal to human-made scans.

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Authors and Affiliations

  1. Institute of Numerical Methods and Informatics in Engineering, Technische Universität Darmstadt, Franziska-Braun-Str. 7, 64287, Darmstadt, Germany

    Robert Irmler, Steffen Franz & Uwe Rüppel

Authors
  1. Robert Irmler
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  2. Steffen Franz
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  3. Uwe Rüppel
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Correspondence to Robert Irmler .

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Editors and Affiliations

  1. Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology, Lausanne (EPFL), Lausanne, Switzerland

    Ian F. C. Smith

  2. Institute for Landscape, Architecture, Construction and Territory (inPact) Construction and Environment Department (CED), University of Applied Sciences, Geneva (HEPIA), Geneva, Switzerland

    Bernd Domer

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Irmler, R., Franz, S., Rüppel, U. (2018). IoT - An Opportunity for Autonomous BIM Reconstruction?. In: Smith, I., Domer, B. (eds) Advanced Computing Strategies for Engineering. EG-ICE 2018. Lecture Notes in Computer Science(), vol 10863. Springer, Cham. https://doi.org/10.1007/978-3-319-91635-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-91635-4_4

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  • Online ISBN: 978-3-319-91635-4

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