In this paper, we introduce an ultra-high frequency radio frequency identification (UHF-RFID) mobile robot platform that is capable of performing fully autonomous inventory taking and stocktaking by providing three-dimensional (3D) product maps and thus making possible the concept of a smart warehouse. The proposed novel hardware architecture consists of an eight-channel UHF-RFID-listener for parallel signal phase recovery, including two different carrier leakage suppression circuits and a correlation decoder for each channel for the tag signal, which can handle a backscatter link frequency (BLF) deviation of up to 22 % to decode the tag data. The system also uses eight parallel channels for multiple-input multiple-output (MIMO) localization. For the system evaluation we labeled clothes stored in a warehouse with tags and generated their product map. The proposed localization algorithm is based on a synthetic aperture radar (SAR) MIMO approach that needs exact knowledge of the antenna positions and, therefore, of the driven trajectory. This position is provided by the robot, which takes advantage of a simultaneous localization and mapping (SLAM) algorithm, determining the position with 1 cm accuracy while generating two-dimensional (2D) maps of the surroundings. We placed ten tags at known positions to assess the system's performance and were able to locate these tags within a root mean square error (RMSE) of 1.45 cm in 3D.

No abstract

No abstract

This paper presents an indoor localization approach that determines the absolute position of a mobile robot platform with centimeter precision by fusing RFID localization results based on cost-effective, standard passive UHF RFID technology with the robot's odometry data. The mobile robot platform is equipped with a multistatic UHF RFID interrogation system, and several RFID tags are arbitrarily placed within the localization environment, serving as landmarks. The RFID localization concept is based on phase evaluations. To overcome the problem of ambiguous position estimations due to the 2πphase ambiguity of the RFID signal phase and mitigate the linearization problem of the nonlinear system, a novel algorithm based on an iterative multihypothesis Kalman filter is introduced. A realistic simulation setup is developed to validate the proposed filter algorithm. By tracking a UHF RFID-equipped mobile robot platform in a real-world office environment, the proposed approach is also practically tested in terms of real-time capability, everyday suitability, and multipath resistance. Given that centimeter precision is only achieved in environments with weak multipath propagation, the RFID localization results are fused with odometry data provided by the robot. This effectively compensates for offset and drift in the odometry sensor, achieving a root-mean-square localization error of 2.7 cm.

No abstract