将视觉标记处理拆分到边缘与云端，提升5G机器人实时性
A Semantic Communication Approach to Fiducial Marker Processing in 5G-Enabled Edge SLAM
- 用语义分割方式将神经网络分在机器人和边缘服务器上
- 关键点估计准确，不同拆分点通信与计算开销可量化
- 适合研究5G+机器人协同感知的开发者参考
自主机器人越来越多依赖边缘计算，在5G网络下卸载高负载感知任务以维持实时性。然而传统视觉标记检测流程难以高效划分任务，不利于面向通信的边缘部署。本文提出一种面向5G边缘SLAM中视觉标记处理的语义拆分推理框架。基于DeepTag的卷积神经网络在机器人与边缘服务器间进行划分，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。该框架集成于基于ROS2的机器人架构，并在真实5G测试床中评估。实验结果表明，关键点估计准确，展示了对下游位姿估计的影响，并量化了不同拆分点带来的通信-计算权衡，为联网机器人系统中深度视觉感知的通信感知部署提供了实用洞察。
原文摘要 · Abstract (English)
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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