Generating Persons Movement Trajectories on a Mobile Robot

For socially interactive robots it is essential to be able to estimate the interest of people to interact with them. Based on this estimation the robot can adapt its dialog strategy to the different people's behaviors. Consequently, efficient and robust techniques for people detection and tracking are basic prerequisites when dealing with human-robot interaction (HRI) in real-world scenarios. In this paper, we introduce an imposed approach for integration of several sensor modalities and present a multimodal, probability-based people detection and tracking system and its application using the different sensory systems of our mobile interaction robot HOROS. For each of these sensory cues, separate and specific Gaussian distributed hypotheses are generated and further merged into a robot-centered map by means of a flexible probabilistic aggregation scheme based on covariance intersection (CI). The main advantages of this approach are the simple extensibility by integration of further sensory channels, even with different update frequencies, and the usability in real-world HRI tasks. Finally, promising experimental results achieved for people tracking in a real-world environment, and university building, are presented