Robust Semantic World Modeling by Beta Measurement Likelihood in a Dynamic Indoor Environment

In this paper, a semantic world model represented by objects and their spatial relationships is considered to endow service robots. In the case of using commercially available visual recognition systems in dynamically changing environments, semantic world modeling must solve problems caused by imperfect measurements. These measurement result from variations caused by moving objects, illumination changes, and viewpoint changes. To build a robust semantic world model, the measurement likelihood method and spatial context representation are addressed to deal with the noisy sensory data, which are handled by temporal confidence reasoning of statistical observation and logical inference, respectively. In addition to the representation of a semantic world model for service robots, formal semantic networks can be exploited in representations that allow for interaction with humans and sharing and re-using of semantic knowledge. The experimental results indicate the validity of the presented novel method for robust semantic mapping in an indoor environment.