Digital Twin of Retail Environment Using RFID Particle Filter Localization

Localization using the RFID technology provides an effective solution for improving inventory accuracy in varying industries. The retail environment being an ever-growing topic of interest, our focus is on exploring Bayesian statistical algorithms centered on particle filter localization. With the use of stereo tracking cameras, we create a 3D point cloud of the environment in real time and localize a variety of items. This method is built upon a previous work that does not rely on RSSI or phase measurements. After the environment is transferred to a Digital Twin using RGB-D cameras, we can identify the position of various tags. To determine the performance of the proposed particle filter, we use a commercial off-the-shelf (COTS) storage rack to emulate a retail environment. A testing procedure is conducted to compare the proposed particle filter approach with the previous Bayesian filter approach. The proposed scheme performs adequately in heavy-clutter environments with a reduced computational time.