Applying rule-based context knowledge to build abstract semantic maps of indoor environments

In this paper, we propose a generalizable method that systematically combines data driven MCMC sampling and inference using rule-based context knowledge for data abstraction. In particular, we demonstrate the usefulness of our method in the scenario of building abstract semantic maps for indoor environments. The product of our system is a parametric abstract model of the perceived environment that not only accurately represents the geometry of the environment but also provides valuable abstract information which benefits highlevel robotic applications. Based on predefined abstract terms, such as “type” and “relation”, we define task-specific context knowledge as descriptive rules in Markov Logic Networks. The corresponding inference results are used to construct a prior distribution that aims to add reasonable constraints to the solution space of semantic maps. In addition, by applying a semantically annotated sensor model, we explicitly use context information to interpret the sensor data. Experiments on real world data show promising results and thus confirm the usefulness of our system.