A Semantic Communication Approach to Fiducial Marker Processing in 5G-Enabled Edge SLAM
2026-08-10 • Networking and Internet Architecture
Networking and Internet ArchitectureRobotics
AI summaryⓘ
The authors studied how to better split the work of recognizing special markers (fiducial markers) between a robot and a nearby edge server using 5G communication. They designed a system where part of a neural network runs on the robot and part runs on the server, sending important feature information instead of all raw data. This helps the robot quickly estimate key points for navigation without overloading the communication link. Their experiments show the method works well for pose estimation and reveals trade-offs between communication needs and computing power.
fiducial markersedge computing5G networkssemantic split inferenceconvolutional neural networkSLAMROS2pose estimationcommunication-computation trade-offdeep visual perception
Authors
Boris Radovanovic, Vukan Ninkovic, Katarina Vidojevic, Buda Bajic Papuga, Dejan Vukobratovic
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.