Modeling RFID signal strength and tag detection for localization and mapping

TLDR

Recent robotics research has focused on whether RFID can solve localization and mapping for mobile robots. The study introduces a novel sensor model for localizing RFID tags and tracking mobile agents, along with an unsupervised bootstrap method to calibrate it. The probabilistic model jointly represents RSSI and tag detection events, achieving higher accuracy than prior models that address only one of these aspects. Real‑world experiments confirm that the approach outperforms existing techniques.

Abstract

In recent years, there has been an increasing interest within the robotics community in investigating whether Radio Frequency Identification (RFID) technology can be utilized to solve localization and mapping problems in the context of mobile robots. We present a novel sensor model which can be utilized for localizing RFID tags and for tracking a mobile agent moving through an RFID-equipped environment. The proposed probabilistic sensor model characterizes the received signal strength indication (RSSI) information as well as the tag detection events to achieve a higher modeling accuracy compared to state-of-the-art models which deal with one of these aspects only. We furthermore propose a method that is able to bootstrap such a sensor model in a fully unsupervised fashion. Real-world experiments demonstrate the effectiveness of our approach also in comparison to existing techniques.