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Interpreting Thermal 3D Models of Indoor Environments for Energy Efficiency

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Abstract

In recent years 3D models of buildings are used in maintenance and inspection, preservation, and other building related applications. However, the usage of these models is limited because most models are pure representations with no or little associated semantics. In this paper we present a pipeline of techniques used for interior interpretation, object detection, and adding energy related semantics to windows of a 3D thermal model. A sequence of algorithms is presented for building the fundamental semantics of a 3D model. Among other things, these algorithms enable the system to differentiate between objects in a room and objects that are part of the room, e.g. floor, windows. Subsequently, the thermal information is used to construct a stochastic mathematical model– namely Markov Random Field– of the temperature distribution of the detected windows. As a result, the MAP(Maximum a posteriori) framework is used to further label the windows as either open, closed or damaged based upon their temperature distribution. The experimental results showed the robustness of the techniques. Furthermore, a strategy to optimize the free parameters is described, in cases where there is a sample training dataset.

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Authors and Affiliations

  1. Interdisciplinary Centre for Security, Reliability and Trust, Université du Luxembourg, Weicker Building, 4, rue Alphonse Weicker, L-2721, Luxembourg, USA

    Girum G. Demisse

  2. Informatics VII : Robotics and Telematics, University of Würzburg, Würzburg, Bavaria, Germany

    Dorit Borrmann & Andreas Nüchter

Authors
  1. Girum G. Demisse
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  2. Dorit Borrmann
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  3. Andreas Nüchter
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Correspondence to Andreas Nüchter.

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Demisse, G.G., Borrmann, D. & Nüchter, A. Interpreting Thermal 3D Models of Indoor Environments for Energy Efficiency. J Intell Robot Syst 77, 55–72 (2015). https://doi.org/10.1007/s10846-014-0099-5

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  • DOI: https://doi.org/10.1007/s10846-014-0099-5

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