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Localizing RFIDs in Pixel Dimensions
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AI
DOI：10.1145/3517012.png)
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Radio Frequency IDentification (RFID) is emerging as a vital technology of the Internet of Things (IoT). Billions of RFID tags have been deployed to locate daily objects such as equipment, pharmaceuticals, vehicles, and so on. Unlike previous solutions that focus on localizing tagged objects in the world coordinate system in reference to reader antennas, this work exploits a system, called RFCamera, that can identify and locate RFID-tagged objects in images with pixel dimensions. Our core insight is that an image is a visual AoA profile in terms of lights, which is resulted from the pinhole camera model. Similarly, we generate an RF image derived from the AoA profile of a tag using the same pinhole model as the camera. Consequently, the locations of visual entities corresponding to tagged objects are highlighted by comparing two types of images. To this end, we customized a camera system equipped with a pair of rotatable reader antennas. Our experimental evaluation demonstrates that RFCamera enables a mean error of 5.7. and 2.9. at azimuth and elevation angle estimation, respectively. It can locate a visual entity with a mean error of 51 pixels (i.e., approximate to 1.3 cm at 96 dpi) in a 640 x 480 image.
Keyword:
Localization
RFID
Robot
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IF:
4.7
论文数:
999
被引数:
2.0K
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