Cooperative Mapping, Localization, and Beam Management via Multi-Modal SLAM in ISAC Systems

TL;DR

This paper introduces a multi-modal SLAM framework for ISAC systems in 6G mmWave networks, integrating cooperative localization, environmental mapping, and beam management to improve accuracy and efficiency.

Contribution

It presents a novel Bayesian multi-user SLAM model, a multi-modal localization approach combining SLAM with camera and IMU data, and a sensing-aided beam management scheme for ISAC systems.

Findings

Radio map accuracy improved by up to 60%

Localization accuracy increased by 37.5%

Outperforms traditional methods in diverse environments

Abstract

Simultaneous localization and mapping (SLAM) plays a critical role in integrated sensing and communication (ISAC) systems for sixth-generation (6G) millimeter-wave (mmWave) networks, enabling environmental awareness and precise user equipment (UE) positioning. While cooperative multi-user SLAM has demonstrated potential in leveraging distributed sensing, its application within multi-modal ISAC systems remains limited, particularly in terms of theoretical modeling and communication-layer integration. This paper proposes a novel multi-modal SLAM framework that addresses these limitations through three key contributions. First, a Bayesian estimation framework is developed for cooperative multi-user SLAM, along with a two-stage algorithm for robust radio map construction under dynamic and heterogeneous sensing conditions. Second, a multi-modal localization strategy is introduced, fusing SLAM results with camera-based multi-object tracking and inertial measurement unit (IMU) data via an error-aware model, significantly improving UE localization in multi-user scenarios. Third, a sensing-aided beam management scheme is proposed, utilizing global radio maps and localization data to generate UE-specific prior information for beam selection, thereby reducing inter-user interference and enhancing downlink spectral efficiency. Simulation results demonstrate that the proposed system improves radio map accuracy by up to 60%, enhances localization accuracy by 37.5%, and significantly outperforms traditional methods in both indoor and outdoor environments.