Multi-domain Cooperative SLAM: The Enabler for Integrated Sensing and Communications

TL;DR

This paper explores multi-domain cooperative SLAM techniques that integrate sensing and communication systems, demonstrating significant improvements in localization accuracy through hybrid sensing, crowdsourcing, and practical testbed validation.

Contribution

It introduces novel multi-domain cooperative SLAM mechanisms for integrated sensing and communications, highlighting their potential to enhance localization accuracy in complex environments.

Findings

Hybrid sensing and crowdsourcing improve localization accuracy

Testbed experiments achieve decimeter-level precision

Multi-domain SLAM enhances situational awareness in ISAC

Abstract

Simultaneous localization and mapping (SLAM) provides user tracking and environmental mapping capabilities, enabling communication systems to gain situational awareness. Advanced communication networks with ultra-wideband, multiple antennas, and a large number of connections present opportunities for deep integration of sensing and communications. First, the development of integrated sensing and communications (ISAC) is reviewed in this study, and the differences between ISAC and traditional communication are revealed. Then, efficient mechanisms for multi-domain collaborative SLAM are presented. In particular, research opportunities and challenges for cross-sensing, cross-user, cross-frequency, and cross-device SLAM mechanisms are proposed. In addition, SLAM-aided communication strategies are explicitly discussed. We prove that the multi-domain cooperative SLAM mechanisms based on hybrid sensing and crowdsourcing can considerably improve the accuracy of localization and mapping in complex multipath propagation environments through numerical analysis. Furthermore, we conduct testbed experiments to show that the proposed SLAM mechanisms can achieve decimeter-level localization and mapping accuracy in practical scenarios, thereby proving the application prospect of multi-domain collaborative SLAM in ISAC.