发表机构
Faculty of Technical Sciences, University of Novi Sad; The Institute for Artificial Intelligence Research and Development of Serbia（诺威萨大学技术科学学院; 塞尔维亚人工智能研究与发展研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对5G赋能边缘SLAM中基准标记处理的通信适配问题，提出DeepTag启发的卷积神经网络拆分推理框架，在机器人与边缘间传输语义特征，经ROS2架构与5G测试平台验证，实现精准关键点估计并量化通信计算权衡。
AI 中文摘要
自主机器人日益依赖边缘计算来卸载计算密集型感知任务，同时在5G网络上保持实时运行。然而，传统基准标记检测流水线提供的高效任务划分机会有限，使其难以适配面向通信的边缘部署。本文提出一种用于5G赋能的边缘SLAM中基准标记处理的语义拆分推理框架。该框架采用受DeepTag启发的卷积神经网络，在机器人与边缘服务器之间进行划分，中间特征表示作为面向任务的语义信息通过无线链路传输。该框架被集成到基于ROS2的机器人架构中，并在真实5G通信测试平台上进行了特性表征。实验结果展示了准确的关键点估计，说明了其对下游位姿估计的影响，并量化了不同拆分点相关的通信-计算权衡，为面向通信的深度视觉感知在联网机器人系统中的部署提供了实用见解。
英文摘要
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.
CommentsAccepted at IEEE CSCN 2026
