MetaSLAM: Wireless Simultaneous Localization and Mapping Using Reconfigurable Intelligent Surfaces

Abstract

Wireless simultaneous localization and mapping (SLAM) has attracted much attention as a promising technique to empower location based services. However, the accuracy of traditional wireless SLAM systems is limited as the wireless signals are easily disturbed by the uncontrollable radio environments. To mitigate this issue, in this paper, we propose a MetaSLAM system where multiple reconfigurable intelligent surfaces (RISs) are deployed to customize the wireless environments. To be specific, through adjusting the phase shifts of these RISs, the strength of reflected signals can be enhanced in order to resist the variance of radio environments. However, it is challenging to coordinate multiple RISs and optimize their phase shifts especially when their locations are unknown to the agent. In order to address these challenges, we formulate a MetaSLAM optimization problem, and design a two-stage optimization algorithm based on the genetic and particle filter algorithms to solve the formulated problem. Analysis of the complexity and the positioning error bound of the proposed SLAM system are provided. Simulation results show that compared with the benchmark schemes, the positioning error obtained by the MetaSLAM system is reduced by at least 31%.