Person Following through Appearance Models and Stereo Vision Using a Mobile Robot

Abstract

Following a person is an important task for mobile service and domestic robots in applications in which human-robot interaction is a primary requirement. In this paper we present an approach that integrates appearance models and stereo vision for efficient people tracking in domestic environments. Stereo vision helps in obtaining a very good segmentation of the scene to detect a person during the automatic model acquisition phase, and to determine the position of the target person in the environment. A navigation module and a high level person following behavior are responsible for performing the task in dynamic and cluttered environments. Experimental results are provided to demonstrate the effectiveness of the proposed approach.