  The primary challenge for any autonomous system operating in realistic,
rather unconstrained scenarios is to manage the complexity and uncertainty of
the real world. While it is unclear how exactly humans and other higher animals
master these problems, it seems evident, that abstraction plays an important
role. The use of abstract concepts allows to define the system behavior on
higher levels. In this paper we focus on the semantic mapping of indoor
environments. We propose a method to extract an abstracted floor plan from
typical grid maps using Bayesian reasoning. The result of this procedure is a
probabilistic generative model of the environment defined over abstract
concepts. It is well suited for higher-level reasoning and communication
purposes. We demonstrate the effectiveness of the approach using real-world
data.
