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.

翻译：本文针对机场机器人自主行李手推车收集中的定位问题，对不同解决方法进行了系统评估。机器人自主行李手推车收集是一个复杂系统，涉及目标检测、定位、运动规划与控制、操作等多个环节。其中，有效定位对机器人后续的运动规划与末端执行器操作至关重要，因为它能提供正确的目标位置。本文综述了四种流行且具代表性的定位方法，包括射频识别（RFID）、关键点（Keypoints）、超宽带（UWB）和反射器（Reflectors），以实现行李收集过程中的目标定位。为测试其性能，我们构建了一个包含定位精度、移动电源、覆盖范围、成本和可扩展性的定性评估框架。此外，我们在真实机器人自主行李手推车收集系统上开展了一系列关于定位精度和成功率的定量实验，并基于实验结果进一步分析了不同定位方法的性能，揭示出关键点方法最适用于室内环境下的行李手推车收集任务。