Extracting Semantic Indoor Maps from Occupancy Grids
This work addresses the need for abstraction in autonomous systems operating in complex indoor environments, though it appears incremental as it builds on existing grid map techniques.
The paper tackles the problem of semantic mapping in indoor environments by proposing a method to extract abstract floor plans from occupancy grids using Bayesian reasoning, resulting in a probabilistic generative model suitable for higher-level reasoning and communication, demonstrated with real-world data.
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.