Robust robot knowledge instantiation for intelligent service robots

Robot knowledge is considered to endow service robots with intelligence. In the real environments, robot knowledge needs to represent dynamically changing world. Despite its advantages for semantic knowledge of service robots, robot knowledge may be instantiated and updated by using imperfect sensing data, such as misidentification of object recognition. In case of using commercially available visual recognition system, incorrect knowledge instances are created and changed frequently due to object misidentification and/or recognition failures. In this work, a robust semantic knowledge handling method under imperfect object recognition is proposed to instantiate and update robot knowledge with logical inference by estimating confidence of the object recognition results. The following properties may be applied to determine misidentifications in logical inference: temporal reasoning to represent relationships between time intervals, statistical reasoning with confidence of object recognition results. To show validity of our proposed method, experimental results are illustrated, where commercial visual recognition system is employed.