Autonomous robots perceive their environment usually through exteroceptive sensors which generate features in the spatial domain. Finding the corresponding semantic or symbolic description can be referred to as the anchoring or grounding problem. Some of the latest research in robotics is dedicated to the generation of semantic maps. This includes labeling of metric maps, which are provided by 3D point clouds. Usually, a model database has to be generated beforehand in order to classify objects in the spatial domain. In our approach, we propose a semantic classification based on object primitives and their spatial 3D relationship. We introduce spatial feature descriptors which can be mapped directly to a symbolic level. By looking at the relationships in the spatial domain, we are able to describe and classify known objects without model learning. The spatial entities can be defined directly using domain knowledge and ontologies. We apply our approach to a mobile dual-manipulator robot with application to logistic scenarios.We propose an ontology-based description of an indoor environment and a probabilistic reasoning approach based on spatial feature descriptions. The paper will give an overview about our current work in progress of applying AI methods to logistics scenarios.
