# Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach +
## 2 Related Work  
To estimate the agent position assuming the knowledge of the passive tag locations in a reference frame, both amplitude and phase of the signal backscattered by the tags and received at the reader side can be profitably used [19; 25]. Several techniques based on the Received Signal Strength Indicator (RSSI) have been proposed, being the more widespread parameter available in commercial readers [19; 26; 27]. In [27], a UHF-RFID system was presented to navigate a robot through a guided path. The robot pose was estimated through a Particle Swarm Optimisation (PSO) algorithm applied to the RSSI data, and the navigation was conducted through a Fuzzy Logic Controller (FLC). A numerical analysis showed that the proposed method is affected by a RMSE of 6 cm when the robot moves in a 36 m\({}^{2}\) room with two reference tags. In [19], the localisation of a mobile robot equipped with two reading antennas is proposed. The method is based on the principle that if an RFID tag is located at the same distance between the two antennas, the correspondent RSSI measurements should ideally be identical. The method shows a centimetre order localisation error for a robot travelling at different speeds in a real indoor scenario of \(5\times 10\) m\({}^{2}\) and exploiting 578 reference tags. However, the signal phase is by far more robust and effective for localisation purposes especially in indoor scenarios [21]. The main challenge to be solved is the ambiguity of phase measurements due to the 2\(\pi\)-periodicity [28], and the knowledge of the phase offset term. The latter is typically solved through the employment of the Phase Difference of Arrival (PDoA) [29]. Indeed, unwrapping techniques [30] or phasor use [31] can face and solve the phase ambiguity issue. In [32], the robot localisation was solved through a Multi-Hypothesis Extended Kalman Filter that combines data acquired from two rotary encoders and RFID phase data gathered by a set of reference tags placed in known locations at the ceiling. Tags were designed with an ad hoc radiation pattern. The method requires for a pre-calibration procedure to estimate the offset term typically affecting the phase measurements. Such calibration procedure is then avoided thanks to the method extension proposed by the same authors in [33], where the RSSI data are employed as additional input parameters. Moreover, such a modification allows reducing the accuracy required for the knowledge of the reference tag positions. According to the experimental results, an average localisation error of centimetre order was obtained within a \(4\times 3\) m\({}^{2}\) room by employing two reference tags. In [20], a novel phase-based sensor-fusion tracking method for moving agents was presented after the European patent application [34]. The robot localisation is achieved through a Synthetic Aperture Radar (SAR) approach [29] by collecting consecutive phase measurements along the agent trajectory with respect to the reference tags. Such an approach allows reducing the density of the reference-tag infrastructure, which can be installed at any position within the indoor scenario. Then, the phase data are combined with the kinematic data collected by odometers, through a sensor-fusion technique. The obtained average localisation error is around 11 cm for a 4 m long trajectory in an indoor office environment by employing two reference tags. The method capability has been validated when odometry measurements are available, but it can be adapted to work with any kind of proprioceptive sensors. Furthermore, it works with COTS devices, and does not require for a calibration procedure. In [35], a particle filter was presented to track a mobile robot equipped with two rotary encoders and two RFID antennas facing to the floor. The algorithm exploits the PDoA gathered by an infrastructure of reference tags deployed on the floor.