[论文解读] When Simultaneous Localization and Mapping Meets Wireless Communications: A Survey

Konstantinos Gounis, Sotiris A. Tegos+3 more|arXiv (Cornell University)|Jan 28, 2026

Robotics and Sensor-Based Localization被引用 0

一句话总结

本综述审视SLAM与无线通信的交叉，强调将视觉SLAM与RF/mmWave感知及ISAC整合，以服务未来的自治系统。

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.

研究动机与目标

• 在基础、方法与应用层面，提供SLAM–无线通信核系的统一分类体系。
• 分析无线信号如何影响SLAM性能，包括基于RF的定位、感知与信道建模。
• 探讨视觉驱动的SLAM（V-SLAM）与无线技术及多传感融合的集成。
• 讨论集成感知、通信与自治的挑战、未解问题与未来方向。

提出的方法

• 综述与SLAM相关的RF传播、几何信道建模与RF基定位的基础概念。
• 给出SLAM和无线感知中使用的概率/状态估计框架（如贝叶斯方法）与运动模型。
• 讨论用于地标检测与建图的计算机视觉技术及其与RF/mmWave信息的融合。
• 回顾基于设备与无设备感知（WiFi、RSSI、CSI、LiDAR、V-SLAM）及它们在联合感知与通信中的作用。
• 强调更新的体系结构与范式（V2C/C2V、MEC卸载、RIS辅助的SLAM、基于DNN的无线SLAM）。

Figure 1: An illustration of the architecture of a networked autonomous system.

实验结果

研究问题

• RQ1 无线信号如何提升SLAM的精度、鲁棒性与可扩展性？
• RQ2 SLAM 技术如何在6G/ISAC场景中增强无线定位、感知与信道理解？
• RQ3 用于联合感知、建图与通信的有效多模态融合策略（视觉、RF、LiDAR、雷达）是什么？
• RQ4 集成SLAM–无线通信系统面临的开放挑战与未来方向是什么？

主要发现

• 单目V-SLAM从RF/mmWave信息中获益，将其作为尺度代理并辅助定位。
• 5G及更高代无线通信在视觉里程计和六自由度位姿估计方法的启发下可获益。
• 多种感知源（WiFi SLAM、RF感知、LiDAR SLAM）可与无线通信集成，形成类似ISAC的概念。
• 用于联合通信与定位的集成解决方案尚处于起步阶段，需要理论与实践方面的进展。
• 本文强调一个跨学科框架，覆盖计算机视觉、RF传播与控制，以服务自治系统。

Figure 2: The dynamics of an autonomous car using Newton-Euler equations and the transport theorem that relates the body frame quantities to earth-fixed frame ones.