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Fingerprinting Localization of RFID tags with Real-Time Performance-Assessment, using a Moving Robot

机译:使用移动机器人通过实时性能评估对RFID标签进行指纹定位

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This work is focused on unmanned inventorying and localization, by deploying an RFID-equipped autonomous robot. The robot is able to perform Simultaneous Localization and Mapping (SLAM), thanks to its optical sensors. As the robot moves inside the target area, it continuously interrogates all RFID tags within range. Passive RFID tags, placed at known locations, are used for the estimation of the locations of the target tags, by properly manipulating the measured backscattered power. The proposed method does not depend on the location of the reader, but only on the locations of the reference tags. Hence, positioning-errors related to SLAM are not accumulated. Mobility of the robot ensures rich collection of measurements. We propose a method for dynamic, real-time configuration of the parameters of the fingerprinting algorithm and real-time evaluation of the localization error of the unknown tags. This is achieved by treating the reference tags as target tags. Thanks to this property, we further exploit mobility of the robot, repeating inventorying and localization in areas, where poor performance is initially recorded. Measurements indicate a mean error of 18cm, with standard deviation of 11cm, deploying a single antenna.
机译:通过部署配备RFID的自主机器人,这项工作集中于无人盘点和本地化。借助其光学传感器,该机器人能够执行同时定位和制图(SLAM)。当机器人在目标区域内移动时,它会连续询问范围内的所有RFID标签。放置在已知位置的无源RFID标签可通过适当地操纵测得的反向散射功率来估算目标标签的位置。所提出的方法不取决于读取器的位置,而仅取决于参考标签的位置。因此,不会累积与SLAM相关的定位误差。机器人的机动性确保了丰富的测量值。我们提出了一种动态,实时配置指纹算法参数和实时评估未知标签定位错误的方法。这是通过将参考标签视为目标标签来实现的。得益于此属性,我们进一步利用了机器人的移动性,在最初记录了较差性能的区域中重复进行库存和本地化。测量表明,使用单个天线时,平均误差为18cm,标准偏差为11cm。

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