LAUSR

Radio-frequency identification (RFID) and computer vision (CV) technology are widely employed in intelligent storage systems for sensing, locating, identifying, and monitoring storage cargo. However, both of them have applicability scenarios and limitations. In this article, we propose a system for spatial perception of storage cargo based on the fusion of RFID and CV data. Specifically, we employ a mobile robot carrying an RFID reader and an RGB camera to move between shelves. The reader is connected to two vertically deployed antennas that receive phase values from the tags on the cargo to perform phase unwrapping. Then, we construct a linear system of equations to solve synthetic aperture radar (SAR)-based 3-D localization to obtain the location of the target tag efficiently and accurately. For the images of the shelves captured by the RGB camera, we use image localization techniques based on color and texture features to obtain the pixel coordinates of the cargo in the image. Since the sampling range of the RFID reader antenna is larger than the shooting range of the RGB camera, we use the coherent point drift (CPD) algorithm with threshold judgment to match the localization results of the two subsystems, enabling us to display the inherent information of the target cargo on the plane rendering. Our proposed system is evaluated through various experiments, and the results show that our localization algorithm achieves high accuracy in 3-D space, and the matching algorithm has high robustness to the number of cargo and missing cargo or tags.