RIS-Enabled SCLAM: An Approach for Simultaneous Radio Communication, Localization, and Mapping

Radio-based simultaneous localization and mapping (SLAM) facilitates unmanned systems to fulfill self-localization and navigation in complex environments. However, the existing studies overlooked communication between devices, which may lead to low efficiency and high costs when performing SLAM tasks. Reconfigurable intelligent surface (RIS) can satisfy the growing demands of users and improve SLAM accuracy in dynamic and complex environments. This article proposes an RIS enabled simultaneous radio communication, localization, and mapping (SCLAM) system, where an unmanned aerial vehicle (UAV) can perform concurrent communication and SLAM with the help of RIS by utilizing communication signals from base station (BS). The proposed method enables the UAV to simultaneously accomplish SLAM and ensure communication with the BS, while also enhancing the spectrum utilization efficiency. We derive the Bayesian Fisher information matrix (BFIM) for joint SLAM and symbol detection of the proposed system, in which the tradeoff between the BFIM of SLAM and the upper bound of the ergodic mutual information is illustrated. Then, a weighted factor-based BFIM is presented to further achieve a performance tradeoff between SLAM and communication. We formulate an optimization problem of joint BS active beamforming and RIS passive beamforming to maximize the log determinant of weighted BFIM. Numerical results verify the superiority of the proposed SCLAM system on position error bound (PEB), mapping error bound (MEB), and spectral efficiency (SE). The performance tradeoff between communication and SLAM is also discussed and explored.