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This paper introduces a novel method for the automatic derivation of building floorplans and indoor models. Our approach is basedon a logical and stochastic reasoning using sparse observations such as building room areas. No further sensor observations like 3Dpoint clouds are needed. Our method benefits from an extensive prior knowledge of functional dependencies and probability densityfunctions of shape and location parameters of rooms depending on their functional use. The determination of posterior beliefs isperformed using Bayesian Networks. Stochastic reasoning is complex since the problem is characterized by a mixture of discrete andcontinuous parameters that are in turn correlated by non-linear constraints. To cope with this kind of complexity, the proposed reasonercombines statistical methods with constraint propagation. It generates a limited number of hypotheses in a model-based top-downapproach. It predicts floorplans based on a-priori localised windows. The use of Gaussian mixture models, constraint solvers andstochastic models helps to cope with the a-priori infinite space of the possible floorplan instantiations

PREDICTION OF BUILDING FLOORPLANS USING LOGICAL AND STOCHASTIC REASONING BASED ON SPARSE OBSERVATIONS