Semantic map partitioning in indoor environments using regional analysis

Abstract

Classification of spatial regions based on semantic information in an indoor environment enables robot tasks such as navigation or mobile manipulation to be spatially aware. The availability of contextual information can significantly simplify operation of a mobile platform. We present methods for automated recognition and classification of spaces into separate semantic regions and use of such information for generation of a topological map of an environment. The association of semantic labels with spatial regions is based on Human Augmented Mapping. The methods presented in this paper are evaluated both in simulation and on real data acquired from an office environment.