AbstractsComputer Science

This doctoral thesis investigates how a robot companion can gain a certain degree of situational awareness through observation and interaction with its surroundings. The focus lies on the representation of the spatial knowledge gathered constantly over time in an indoor environment. However, from the background of research on an interactive service robot, methods for deployment in inference and verbal communication tasks are presented. The design and application of the models are guided by the requirements of referential communication. The approach here involves the analysis of the dynamic properties of structures in the robot’s field of view allowing it to distinguish objects of interest from other agents and background structures. The use of multiple persistent models representing these dynamic properties enables the robot to track changes in multiple scenes over time to establish spatial and temporal references. This work includes building a coherent representation considering allocentric and egocentric aspects of spatial knowledge for these models. Spatial analysis is extended with a semantic interpretation of objects and regions. This top-down approach for generating additional context information enhances the grounding process in communication. A holistic, boosting-based classification approach using a wide range of 2D and 3D visual features anchored in the spatial representation allows the system to identify room types. The process of grounding referential descriptions from a human interlocutor in the spatial representation is evaluated through referencing furniture. This method uses a probabilistic network for handling ambiguities in the descriptions and employs a strategy for resolving conflicts. In order to approve the real-world applicability of these approaches, this system was deployed on the mobile robot BIRON in a realistic apartment scenario involving observation and verbal interaction with an interlocutor. Advisors/Committee Members: Wachsmuth, Sven (advisor).