# Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach +
## 1 Introduction  
The growth of Industry 4.0 applications lays the groundwork for the development of new services and solutions [1]. In this scenario, the lion's share is likely to be taken by a new generation of autonomous vehicles, capable of operating in complex and dynamic industrial scenario with high flexibility and reliability. One of the most critical functionalities needed to enable these new services is the so-called localisation, defined as the ability to maintain an accurate estimate of each robot pose in time [2]. Since the large majority of production plants are actually indoor, localisation is almost entirely intended as indoor localisation [3, 4]. Given the absence of a reliable Global Position System inside a plant or an indoor logistic area, several technologies have been proposed to deliver a reliable localisation service. Laser Imaging Detection and Ranging (LiDAR) [5], cameras [6, 7], ultrasounds [8] or Radio Frequency (RF) systems [9] are available options used in the past. In the last category, particularly fall solutions with different performance and costs such as Wi-Fi [10], Bluetooth [11], Ultra Wide Band (UWB) [12, 13] or RF Identification (RFID) [14, 15] technologies. The ultra-high-frequency (UHF) RFID technology is gaining traction due to its being multipurpose (a RFID tag can be used both to identify a pallet and to localise it), to its low-cost, and to its easy installation and maintenance. The system operates with radio-waves in the band between 860 MHz to960 MHz, which can penetrate through many materials without significant alterations. This feature provides the system with an intrinsic level of robustness, and marks a remarkable difference with respect to technologies operating at higher frequencies or adopting line-of-sight equipments, such as LiDARs. As an additional remark, the use of RFID makes the system immune from any type of privacy concerns, which could be present in camera-based systems. Two types of solutions have been conceived in RFID-based vehicle localisation. The first category exploits an infrastructure of fixed reader antennas, with the robot being equipped with an RFID tag [16]. The second one, on the contrary, is based on having the vehicle equipped with a reader connected to one or more antennas, while a grid of RFID tags is deployed in the surrounding scenario at known locations [17]. In case of unknown reference tag positions, simultaneous localisation and mapping (SLAM) systems has been also designed [18]. For sure, the second class of solutions is arguably more appealing to a possible implementer since the passive UHF-RFID tags do not require direct power supply (contrary to the UWB nodes). The data association can be easily performed thanks to the tag unique identifier (Electronic Product Code, EPC). Moreover, UHF-RFID tags can be easily installed at the floor side [19], at the ceiling [18] or in almost any other place in the considered environment [20]. In harsh industrial scenarios with lots of metallic structures, Commercial-Off-The-Shelf (COTS) on metal tags can be employed. Finally, when the vehicle is natively equipped with COTS RFID hardware, e.g., the RFID-robot employed for inventory tasks [21], the implementation of the RFID-based localisation infrastructure amounts just to reference tag deployment. A detailed survey of solutions based on the RFID technology for vehicle localisation has been presented by [PERSON] et al. in [22]. At the state-of-the-art, systems rely only on the employment of an RFID infrastructure or on sensor-fusion solutions employing both RFID technology with proprioceptive sensors [17] or both RFID systems with other exteroceptive sensors [23]. Measured data are typically combined through dynamic estimation algorithms based for example on Kalman filters or Particle filters. The most representative and investigated category is based on UHF-RFID systems combined with proprioceptive sensors, e.g., encoders, Inertial Measurement Units (IMUs) or optical flow sensors.