Extracting semantic indoor maps from occupancy grids

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 us 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.

► This work proposes a semantic generative model in the form of a scene graph for indoor 2D occupancy grid maps.
► This model is derived by doing probabilistic inference combining top-down and bottom-up methods.
► This work attempts to bridge the gap between high-level knowledge reasoning and low-level data processing.