Sensor fusion based human detection and tracking system for human-robot interaction

Abstract

Service robot has received enormous attention with rapid development of advanced technology in recent years, and it is endowed with the capabilities of performing human-robot interaction (HRI). We construct a sensor fusion based system to integrate the information from both sensors by using a data association approach - Covariance Intersection (CI). It will be used to increase the robustness and reliability of HRI in the real world environment. In this paper, we propose a Behavior System for analyzing human features and classifying the behavior by the crucial information from sensor fusion system. The system is used to infer the human behavioral intentions, and also allow the robot to perform more natural and intelligent interaction. We apply a spatial model based on proxemics rules to our robot, and design a behavioral intention inference strategy. Furthermore, the robot will make the corresponding reaction in accordance with the identified behavioral intention.