RFID-based hybrid metric-topological SLAM for GPS-denied environments

In this work, we propose a novel RFID-based hybrid metric-topological Simultaneous Localization and Mapping (SLAM) algorithm which enables autonomous navigation in GPS-denied environments. A method based on the normalized-cut is proposed for online clustering of strongly connected Radio Frequency Identification (RFID) tags to form topological nodes. A particle filter together with a sensor model which characterizes the received signal strength (RSS) as well as the tag detection probability is used to create metric submaps for each topological node. The hybrid framework is highly scalable, simplifies path planning and promises precision and robustness. The algorithm requires only odometry and RFID measurements to localize the RFID tags with a relative accuracy of approximately 0.3 meters. The ideas presented here are supported by experimental results.