2D Semantic Mapping on Occupancy Grids

In recent years, techniques for building metric maps of indoor environments have been intensely studied, and they perform very well in noumerous applications. Simultaneous Localization and Mapping (SLAM) methods produce globally consistent, metric maps of the explored environment. Although such maps describe how the environment looks like and can be used for navigation, there exist no abstracted semantic concepts that explain the environment on a higher level or in a more natural way (as we humans do), such as, what kind of structure and connectivity the environment possesses. In this paper, we propose a new probabilistic method to analyze the underlying semantic world model based on an occupancy grid map, which is generated by a standard SLAM process. Our approach simulates a Markov Chain that produces samples from the distribution of probable semantic world models given an input map. Experiments show that our approach is effective and correctly captures the uncertainty.