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Semantic mapping of indoor environments refers to the task of building representations of these environments that associate spatial concepts with spatial entities. In particular, semantic labels, like ‘rooms’ and ‘corridors’ are associated to portions of an underlying metric map, to allow robots or humans to exploit this additional knowledge. Usually, the classifiers that build semantic maps process data coming from laser range scanners and cameras and do not consider the specific type of the mapped building. However, in architecture it is well known that each building has a specific typology. The concept of building typology denotes the set of buildings that have the same function (e.g., being a school building) and that share the same structural features. In this paper, we exploit the concept of building typology to build semantic maps of indoor environments. The proposed system uses only data from laser range scanners and creates a specific classifier for each building typology, showing good classification accuracy.

A System for Building Semantic Maps of Indoor Environments Exploiting the Concept of Building Typology