MULTIMODAL PEOPLE TRACKING AND IDENTIFICATION FOR SERVICE ROBOTS

Abstract

In order for a service robot to approach humans and provide the services it has been designed for, an efficient system for people tracking and identification must be developed. This paper presents a novel solution to the problem that makes use of different sensors and data fusion techniques. The robot utilizes a laser device and a PTZ color camera to detect, respectively, human legs and faces. The relative information is integrated, in real-time, using a sequential implementation of Unscented Kalman Filter. Furthermore, thanks to an histogram comparison with a measure based on the Bhattacharyya coefficient, people are also identified and labelled according to their clothes. This measure is also used to improve the robustness of the data association process. The effectiveness of the proposed method is shown by experiments with a real mobile robot in challenging situations.