Robust Simultaneous Localization and Mapping Using Range and Bearing Estimation of Radio Ultra High Frequency Identification Tags

Abstract

In this article, we consider an indoor simultaneous localization and mapping (SLAM) problem for a mobile robot measuring the phase of the signal backscattered by a set of passive radio ultra high frequency identification (ID) tags, deployed in unknown position on the ceiling of the environment. The solution approach is based on the introduction, for each radio frequency identification (RFID) tag observed, of a multihypothesis extended Kalman filter (MHEKF) which, based on the measured phases and on the wheel encoder readings, provides an estimate of the range and of the bearing of the observed tag with respect to the robot. This information is then used in an extended Kalman filter (EKF) solving the SLAM problem. Since an effective range and bearing estimate is available only after some steps, a resilient module is added to the algorithm to evaluate the reliability of the position estimate of each observed tag. As shown through numerical and experimental results, this makes the proposed approach robust with respect to several kinds of unmodeled disturbances, like multipath effects or even the unexpected change of the position of a tag.

CM-SLASM: A Cooperative Multitechnology Simultaneous Localization and Signal Mapping for Vehicles Indoor Positioning

“…Other important works in the field of indoor positioning and SLAM are represented by [36] and [37], where the authors, considering SLAM problems, propose a multihypothesis extended Kalman filter (MHEKF) techniques. A technique based on Map-Point Consensus-based Outlier Rejection (MC-OR) for sparse-indirect SLAM systems is provided in [38].…”

The Role of the RFID Polarization Mismatch in the Simultaneous Localization and Mapping Problem

“…The solution approach to solve the SLAM problem defined in Section II is based on the algorithm presented in [15], which is modified to take into account the dependence of the phases on the antenna's orientation in the measurement model. The details of the algorithm can be found in [15].…”

“…The change in the phases due to this phenomenon can also be considered as a time varying offset in the phase measurements (see also [13]), which can be included among the estimated variables, as done, e.g., in [14], [15].…”

“…The SLAM solution approach considered in this paper is based on the algorithm presented in [15], which is slightly modified to deal with the different measurement model, where the phenomenon of the dependence of the phase measurements on the antenna's orientation is present. This phenomenon, to the best of the authors knowledge, has never been considered before in the literature in a SLAM context.…”

“…In Section III we present the approach proposed to solve the problem defined in Section II. Some numerical results are reported in Section IV, to compare the proposed approach with the method presented in [15] (Section IV-A) and to investigate about possible benefits on the SLAM problem which may come from the dependence of the phase measurements on the antenna's orientation (Section IV-B). A detailed discussion on the simulation results, focusing on the comparison amongst the various cases analyzed in the paper, is presented in Section IV-C. An experimental comparison with the approach proposed in [15] is reported in Section V while some concluding remarks are drawn in Section VI.…”