When Simultaneous Localization and Mapping Meets Wireless Communications: A Survey

2026-05-22 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

This survey comprehensively analyzes the intersection of Simultaneous Localization and Mapping (SLAM) and wireless communications, particularly focusing on their bidirectional impact and integration with visual SLAM (V-SLAM) and millimeter-wave (mmWave) technologies. It covers fundamental concepts like wireless signal propagation, geometric channel modeling, and RF-based localization, alongside image processing techniques for landmark detection. The study details mathematical approaches such as Bayesian filters and probabilistic state estimation, and explores various SLAM modalities including Radio SLAM, WiFi SLAM, and LiDAR SLAM. Key findings indicate that monocular V-SLAM significantly benefits from RF/mmWave information for scale ambiguity resolution, while 5G and beyond wireless communications can leverage SLAM's visual odometry and 6-D pose estimation. The survey also addresses critical challenges in dynamic environments, latency, energy efficiency, security, and privacy for SLAM-enabled networked autonomous systems.

Key takeaway

For robotics engineers developing autonomous systems, integrating wireless communication capabilities directly into SLAM frameworks is essential. You should prioritize hybrid sensor fusion, combining visual, inertial, and RF/mmWave data to enhance localization accuracy and robustness in dynamic environments. This approach mitigates single-modality limitations and is critical for addressing latency, energy efficiency, and security challenges inherent in 6G-enabled networked autonomy.

Key insights

SLAM and wireless communications mutually enhance autonomous systems' localization, mapping, and control capabilities.

Principles

• Wireless signals inherently augment the vehicle state vector in SLAM.
• Communication feedback loops convey geometric information for spatial inference.
• Active SLAM optimizes agent motion to minimize estimation uncertainty.

Method

Vector-field SLAM models continuous signal variations (e.g., WiFi RSS) using a regular grid, bilinear interpolation, and a nonlinear state-space model for robot pose and measurements.

In practice

• Utilize RF/mmWave data to resolve scale ambiguity in monocular V-SLAM.
• Leverage 5G/6G networks for real-time, cloud-based SLAM computations.
• Deploy Reconfigurable Intelligent Surfaces (RIS) for programmable SLAM services.

Topics

• Simultaneous Localization and Mapping
• Wireless Communications
• Millimeter-Wave
• 5G/6G Networks
• Autonomous Systems
• Sensor Fusion
• Reconfigurable Intelligent Surfaces

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Robotics Engineer, AI Engineer

Related on AIssential

• AI Isn’t the Real Bottleneck in Autonomy; Wireless Is — Reliable wireless communication, not AI, is the critical bottleneck for real-world autonomous systems.
• Camera-RFID Fusion for Robust Asset Tracking in Forested Environments — Fusing camera vision and RFID data enables robust, high-accuracy asset tracking in complex environments by overcoming individual sensor limitations.
• Ten Technology Enablers Shaping the Future of 6G Wireless — The future of 6G wireless relies on ten converging technologies, from THz to AI/ML and RIS, for 1 Tbps data rates.
• See everything, always: Manufacturing precise positioning with private 5G — Private 5G networks integrate precise indoor positioning directly into communication infrastructure, enhancing manufacturing operational visibility.
• An Infrastructure-less, Control-Independent Solution to Relative Localisation of a Team of Mobile Robots using Ranging Measurements — The MHDCL algorithm enables robust, infrastructure-less multi-robot localization by maintaining multiple pose hypotheses without motion control.
• X-Band UAV-enabled Integrated Sensing and Communications for Vehicular Networks — Optimal time allocation in UAV-enabled ISaC systems dynamically balances sensing and communication under varying channel conditions.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.