From Structure to Actions: Semantic Navigation Planning in Office Environments

The use of meaning in mapping and navigation is inevitable if a robot has to interact with its environment in a goal-directed way. Moreover, a semantic environment model makes navigation planning more efficient and simplifies the review and communication of the robot’s knowledge. Existing work in this area decomposes the environment into places, which can be distinguished using the robot’s sensors. However, if important features of the environment cannot be detected by the robot’s sensors a different approach is needed. This paper introduces the Semantic Region Map, an environment model with complex metric, topological and semantic features. It shows how navigation points, so-called semantic positions, can be deduced from the map using a semantic description of the environment. Furthermore, the semantic positions are connected to a reachability graph, whose edges are labelled with robot actions, using a semantic description of the robot’s capabilities. An ontology consisting of a taxonomy and a set of rules are used to implement the semantic models. The concept of the Semantic Region Map is applied to a robot operating in an office environment.