When Simultaneous Localization and Mapping Meets Wireless Communications: A Survey

2026-01-28

arXiv.org

AI Summary

This study addresses the key challenges and synergistic mechanisms in integrating simultaneous localization and mapping (SLAM) with wireless communication, with a focus on the bidirectional enhancement between visual SLAM and radio frequency (RF) signals for state estimation, scale recovery, and perception augmentation. Through a systematic review of wireless channel modeling, RF-aided localization, visual feature extraction, and perception-driven motion control, this work presents the first comprehensive synthesis of their mutual benefits: monocular visual SLAM can resolve scale ambiguity using RF information, while 5G+ communication systems can leverage visual odometry to enhance performance. By integrating geometric channel modeling, Bayesian filtering, and multi-sensor fusion, the study establishes a unified theoretical framework for joint communication, perception, and localization, offering a novel pathway toward high-precision state estimation for autonomous robots.

Abstract

The availability of commercial wireless communication and sensing equipment combined with the advancements in intelligent autonomous systems paves the way towards robust joint communications and simultaneous localization and mapping (SLAM). This paper surveys the state-of-the-art in the nexus of SLAM and Wireless Communications, attributing the bidirectional impact of each with a focus on visual SLAM (V-SLAM) integration. We provide an overview of key concepts related to wireless signal propagation, geometric channel modeling, and radio frequency (RF)-based localization and sensing. In addition to this, we show image processing techniques that can detect landmarks, proactively predicting optimal paths for wireless channels. Several dimensions are considered, including the prerequisites, techniques, background, and future directions and challenges of the intersection between SLAM and wireless communications. We analyze mathematical approaches such as probabilistic models, and spatial methods for signal processing, as well as key technological aspects. We expose techniques and items towards enabling a highly effective retrieval of the autonomous robot state. Among other interesting findings, we observe that monocular V-SLAM would benefit from RF relevant information, as the latter can serve as a proxy for the scale ambiguity resolution. Conversely, we find that wireless communications in the context of 5G and beyond can potentially benefit from visual odometry that is central in SLAM. Moreover, we examine other sources besides the camera for SLAM and describe the twofold relation with wireless communications. Finally, integrated solutions performing joint communications and SLAM are still in their infancy: theoretical and practical advancements are required to add higher-level localization and semantic perception capabilities to RF and multi-antenna technologies.

Problem

• SLAM
• Wireless Communications
• Visual Odometry
• RF-based Localization
• Scale Ambiguity

Innovation

• SLAM
• Wireless Communications
• Visual SLAM
• RF-based Localization
• Sensor Fusion