A multi-modal person perception framework for socially interactive mobile service robots

Müller, Steffen GND; Wengefeld, Tim; Trinh, Thanh Quang; Aganian, Dustin; Eisenbach, Markus; Gross, Horst-Michael GND

In order to meet the increasing demands of mobile service robot applications, a dedicated perception module is an essential requirement for the interaction with users in real-world scenarios. In particular, multi sensor fusion and human re-identification are recognized as active research fronts. Through this paper we contribute to the topic and present a modular detection and tracking system that models position and additional properties of persons in the surroundings of a mobile robot. The proposed system introduces a probability-based data association method that besides the position can incorporate face and color-based appearance features in order to realize a re-identification of persons when tracking gets interrupted. The system combines the results of various state-of-the-art image-based detection systems for person recognition, person identification and attribute estimation. This allows a stable estimate of a mobile robot’s user, even in complex, cluttered environments with long-lasting occlusions. In our benchmark, we introduce a new measure for tracking consistency and show the improvements when face and appearance-based re-identification are combined. The tracking system was applied in a real world application with a mobile rehabilitation assistant robot in a public hospital. The estimated states of persons are used for the user-centered navigation behaviors, e.g., guiding or approaching a person, but also for realizing a socially acceptable navigation in public environments.


Citation style:
Müller, S., Wengefeld, T., Trinh, T.Q., Aganian, D., Eisenbach, M., Gross, H.-M., 2020. A multi-modal person perception framework for socially interactive mobile service robots. Sensors 20, 2020, art. 722. https://doi.org/10.3390/s20030722
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