A MultiModal Person Perception Framework for Socially Interactive Mobile Service Robots 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 realworld scenarios. In particular, multi sensor fusion and human reidentification 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 probabilitybased data association method that besides the position can incorporate face and colorbased appearance features in order to realize a reidentification of persons when tracking gets interrupted. The system combines the results of various stateoftheart imagebased detection systems for person recognition, person identification and attribute estimation. This allows a stable estimate of a mobile robotxe2x80x99s user, even in complex, cluttered environments with longlasting occlusions. In our benchmark, we introduce a new measure for tracking consistency and show the improvements when face and appearancebased reidentification 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 usercentered navigation behaviors, e.g., guiding or approaching a person, but also for realizing a socially acceptable navigation in public environments.

Map Based Human Motion Prediction for People Tracking Mobile service robots deployed in populated environments like train stations, airports or offices are not only required to move safely, but also in socially acceptable ways. In order to achieve this, robots need to be able to track people within their vicinity. This work presents an approach to tracking people from a mobile robot platform, incorporating a novel approach to human motion prediction. Unfavorable viewing angles, motion blur and everchanging light conditions are constant issues for sensors on mobile vehicles. Therefore, a system is needed which increases the tracking quality of humans in order to cope with a low detection rate. The scientific contribution of this paper lies in a precise human model for tracking which utilizes historical spatial data of pedestrians from previous detection and from simulation. The model is embedded into a particlefilter based tracking approach designed for the use on a moving platform and is able to incorporate a variety of person detectors. Experiments conducted prove that the proposed method increases tracking quality, especially at a low detection rate.