When Simultaneous Localization and Mapping Meets Wireless Communications: A Survey

TL;DR

This survey examines the emerging cross-disciplinary area where SLAM and wireless communications reinforce each other, with a focus on visual SLAM integration and RF/mmWave sensing. It organizes the landscape into foundations for networked autonomy, SLAM modalities and methods, and wireless-enabled perception techniques, highlighting bidirectional benefits such as RF signals aiding scale estimation for monocular V-SLAM and vision-driven VO improving channel prediction for wireless systems. The work surveys probabilistic and graph-based SLAM foundations, multimodal sensor fusion, and DNN-driven wireless SLAM, including RIS-aided and ISAC-enabled paradigms, while identifying core challenges in dynamics, latency, energy, and security. Overall, the paper argues that joint SLAM–communication systems will be foundational for 6G-enabled autonomous systems, necessitating integrated, predictive, and edge-empowered designs that fuse perception, control, and connectivity.

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.

Figures (7)

• Figure 1: An illustration of the architecture of a networked autonomous system.
• 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.
• Figure 3: Multi-dimensional channel estimation, channel parameter clustering and SLAM using a novel likelihood function that accounts for both specular and diffuse multipath components.
• Figure 4: Vector Field SLAM: The bilinear interpolation function of the expected signal values from the four nodes of the cell containing the robot, based on Gutmann2014_VectorFieldSLAM_Extensions.
• Figure 5: Area graph localization (AGloc) pipeline based on Xie2023_LifelongLiDAR_Localization_AreaGraph.