MetaSLAM: Wireless Simultaneous Localization and Mapping Using Reconfigurable Intelligent Surfaces

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%.

I. Introduction

Future 6G networks are expected to enable a range of location and sensing based services such as autonomous driving [2] and UAV navigation [3]. To fulfill this vision, simultaneous localization and mapping (SLAM) that estimates the position of the user and builds up the map of an unknown environment is a promising technique [4]. Based on the sensing methods, SLAM systems can be divided into three categories, i.e., visual SLAM, lidar SLAM, and wireless SLAM systems. Among these SLAM systems, wireless SLAM systems which use antennas to exploit the multipath effect of the wireless signals are gaining increasing attention due to the advantages of robustness to poor lighting and low cost. Specifically, in dark scenarios, visual SLAM usually fails due to the low resolution of the captured images, while the performance of wireless SLAM is barely affected. Moreover, the cost of the dedicated lidar components for lidar SLAM is high, while the cost of antennas are much cheaper and existing wireless infrastructures can be leveraged in wireless SLAM systems [5].