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Link to original content: https://doi.org/10.48550/arXiv.2303.06551
[2303.06551] A Systematic Evaluation of Different Indoor Localization Methods in Robotic Autonomous Luggage Trolley Collection at Airports
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Computer Science > Robotics

arXiv:2303.06551 (cs)
[Submitted on 12 Mar 2023]

Title:A Systematic Evaluation of Different Indoor Localization Methods in Robotic Autonomous Luggage Trolley Collection at Airports

Authors:Zhirui Sun, Weinan Chen, Jiankun Wang, Max Q.-H. Meng
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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. In this article, we survey four popular and representative localization methods to achieve object localization in the luggage collection process, including radio frequency identification (RFID), Keypoints, ultrawideband (UWB), and Reflectors. To test their performance, we construct a qualitative evaluation framework with Localization Accuracy, Mobile Power Supplies, Coverage Area, Cost, and Scalability. Besides, we conduct a series of quantitative experiments regarding Localization Accuracy and Success Rate on a real-world robotic autonomous luggage trolley collection system. We further analyze the performance of different localization methods based on experiment results, revealing that the Keypoints method is most suitable for indoor environments to achieve the luggage trolley collection.
Subjects: Robotics (cs.RO)
Cite as: arXiv:2303.06551 [cs.RO]
  (or arXiv:2303.06551v1 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2303.06551

Submission history

From: Zhirui Sun [view email]
[v1] Sun, 12 Mar 2023 03:31:10 UTC (8,892 KB)
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