Deep-Learning-Based Indoor Human Following of Mobile Robot Using Color Feature release_2gainsmhbbh3jfigqd4f757tgm

by Redhwan Algabri, Mun-Taek Choi

Published in Sensors by MDPI AG.

2020   Volume 20, Issue 9, p2699

Abstract

Human following is one of the fundamental functions in human–robot interaction for mobile robots. This paper shows a novel framework with state-machine control in which the robot tracks the target person in occlusion and illumination changes, as well as navigates with obstacle avoidance while following the target to the destination. People are detected and tracked using a deep learning algorithm, called Single Shot MultiBox Detector, and the target person is identified by extracting the color feature using the hue-saturation-value histogram. The robot follows the target safely to the destination using a simultaneous localization and mapping algorithm with the LIDAR sensor for obstacle avoidance. We performed intensive experiments on our human following approach in an indoor environment with multiple people and moderate illumination changes. Experimental results indicated that the robot followed the target well to the destination, showing the effectiveness and practicability of our proposed system in the given environment.
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Type  article-journal
Stage   published
Date   2020-05-09
Language   en ?
DOI  10.3390/s20092699
PubMed  32397411
PMC  PMC7273221
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