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

• URL: http://arxiv.org/abs/2608.09620v1
• Date: Mon, 10 Aug 2026 13:59:11 GMT
• Title: A Semantic Communication Approach to Fiducial Marker Processing in 5G-Enabled Edge SLAM
• Abstract summary: This paper proposes a semantic split inference framework for fiducial marker processing in 5G-enabled Edge SLAM.<n>The framework is integrated into a ROS2-based robotic architecture and characterized over a real 5G communication testbed.
• Score: 1.4136330551561624
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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