A Semantic Communication Approach to Fiducial Marker Processing in 5G-Enabled Edge SLAM

2026-08-10

AI Summary

This work addresses the challenge of achieving efficient communication–perception co-design in 5G-enabled edge SLAM, where conventional fiducial marker detection struggles to balance accuracy and resource constraints. To this end, the paper introduces a semantic segmentation inference framework that, for the first time, integrates semantic communication principles into fiducial processing for edge SLAM. Built upon a DeepTag-inspired CNN, the framework dynamically partitions the model between the robot and an edge server, transmitting task-oriented intermediate semantic features over wireless links to unify communication and perception. Evaluated on a 5G testbed with a ROS2-based robotic platform, the approach demonstrates high-precision keypoint estimation and its positive impact on pose estimation, while also quantifying the communication–computation trade-offs across different model split points, offering practical guidance for deploying visual perception in connected robotic systems.

Abstract

Autonomous robots increasingly rely on edge computing to offload computationally intensive perception tasks while maintaining real-time operation over 5G networks. However, conventional fiducial marker detection pipelines provide limited opportunities for efficient task partitioning, making them poorly suited for communication-aware edge deployment. This paper proposes a semantic split inference framework for fiducial marker processing in 5G-enabled Edge SLAM. A DeepTag-inspired convolutional neural network is partitioned between the robot and the edge server, where intermediate feature representations serve as task-oriented semantic information transmitted over the wireless link. The framework is integrated into a ROS2-based robotic architecture and characterized over a real 5G communication testbed. Experimental results demonstrate accurate keypoint estimation, illustrate the impact on downstream pose estimation, and quantify the communication--computation trade-offs associated with different split points, providing practical insights for communication-aware deployment of deep visual perception in connected robotic systems.

Problem

• fiducial marker
• edge SLAM
• semantic communication
• task partitioning
• 5G-enabled robotics

Innovation

• semantic communication
• edge SLAM
• split inference
• fiducial marker
• 5G-enabled robotics