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A systematic evaluation of different indoor localization methods in robotic autonomous luggage trolley collection at airports

Published online by Cambridge University Press:  28 November 2024

Zhirui Sun
Affiliation:
Shenzhen Key Laboratory of Robotics Perception and Intelligence, and the Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China
Weinan Chen
Affiliation:
Guangdong University of Technology, Guangzhou, China
Can He
Affiliation:
Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China
Jiankun Wang*
Affiliation:
Shenzhen Key Laboratory of Robotics Perception and Intelligence, and the Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China
*
Corresponding author: Jiankun Wang; Email: wangjk@sustech.edu.cn

Abstract

This article addresses the localization problem in robotic autonomous luggage trolley collection at airports and provides a systematic evaluation of different methods to solve it. The robotic autonomous luggage trolley collection is a complex system that involves object detection, localization, motion planning and control, manipulation, etc. Among these components, effective localization is essential for the robot to employ subsequent motion planning and end-effector manipulation because it can provide a correct goal position. This article explores four popular and representative localization methods for object localization in luggage trolley collection: radio frequency identification (RFID), Keypoints, ultrawideband (UWB), and Reflectors. A qualitative evaluation framework is constructed to assess performance, encompassing Localization Accuracy, Mobile Power Supplies, Coverage Area, Cost, and Scalability. Furthermore, a series of quantitative experiments concerning Localization Accuracy and Success Rate have been conducted on a real-world robotic autonomous luggage trolley collection system. The performance of various localization methods is further analyzed based on experimental results, indicating that the Keypoints method is optimally suited for indoor environments to facilitate luggage trolley collection. Significantly, these experiment results provide a valuable reference point, extending the application of indoor localization methods across diverse scenarios. A website about this work is available at https://sites.google.com/view/localization-evaluation/.

Information

Type
Research Article
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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