ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W1, 265-270, 2016
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W1/265/2016/
doi:10.5194/isprs-annals-IV-2-W1-265-2016
 
05 Oct 2016
PREDICTION OF BUILDING FLOORPLANS USING LOGICAL AND STOCHASTIC REASONING BASED ON SPARSE OBSERVATIONS
S. Loch-Dehbi1, Y. Dehbi1, G. Gröger2, and L. Plümer1 1Institute of Geodesy and Geoinformation, University of Bonn, Meckenheimer Allee 172, Bonn, Germany
2CPA Software GmbH, Auf dem Seidenberg 3a, 53721 Siegburg, Germany
Keywords: Floorplan, stochastic reasoning, Gaussian mixture, Bayesian networks, Constraint logic programming Abstract. This paper introduces a novel method for the automatic derivation of building floorplans and indoor models. Our approach is based on a logical and stochastic reasoning using sparse observations such as building room areas. No further sensor observations like 3D point clouds are needed. Our method benefits from an extensive prior knowledge of functional dependencies and probability density functions of shape and location parameters of rooms depending on their functional use. The determination of posterior beliefs is performed using Bayesian Networks. Stochastic reasoning is complex since the problem is characterized by a mixture of discrete and continuous parameters that are in turn correlated by non-linear constraints. To cope with this kind of complexity, the proposed reasoner combines statistical methods with constraint propagation. It generates a limited number of hypotheses in a model-based top-down approach. It predicts floorplans based on a-priori localised windows. The use of Gaussian mixture models, constraint solvers and stochastic models helps to cope with the a-priori infinite space of the possible floorplan instantiations.
Conference paper (PDF, 2067 KB)


Citation: Loch-Dehbi, S., Dehbi, Y., Gröger, G., and Plümer, L.: PREDICTION OF BUILDING FLOORPLANS USING LOGICAL AND STOCHASTIC REASONING BASED ON SPARSE OBSERVATIONS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W1, 265-270, doi:10.5194/isprs-annals-IV-2-W1-265-2016, 2016.

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