A Hybrid Human Tracking System using UWB Sensors and Monocular Visual Data Fusion for Human Following Robots

Abstract

The ability to follow people can benefit the human-robot interaction of mobile robots. This work proposes a hybrid human tracking system for human following robots, integrating sensor fusion of Ultra-Wideband (UWB) and monocular visual positioning to enhance tracking accuracy and precision. At the same time, UWB and the visual positioning system can operate independently, thereby creating a redundancy in the system. Based on our previous study of UWB-positioning, this article elaborates on a visual positioning system that employs human detection using a pre-trained Convolutional Neural Network (CNN), coupled with data fusion process based on experimental assessments. The hybrid human tracking system achieves a 2D Euclidean accuracy RMS of 7.4 cm, demonstrating sufficient accuracy for human following and improving the following performance in real-world experiments compared to our previous study.