用边缘计算实现高精度手势控制无人机，抗干扰能力强。
UAV Control with Vision-based Hand Gesture Recognition over Edge-Computing
- 基于手部关键点识别手势，适应远距离与复杂环境。
- 在仿真与实机测试中均实现稳定实时控制，性能优于传统方法。
- 适合需要自然交互的无人机应用，如救援、巡检场景。
手势识别因其直观性与精准交互潜力，成为人机交互无人机控制的有前景方向。现有基于裁剪、缩放与颜色分割的手势识别方法在动态环境下表现不佳，随距离增加和环境噪声增强而性能下降。本文提出一种新方法，利用手部关键点绘制与分类进行手势识别，用于无人机控制。实验表明，该方法在准确率、抗噪能力及跨距离适应性方面均优于现有技术，提供更稳健的控制决策。然而，在无人机机载计算机上部署深度学习等计算密集型算法面临性能挑战。为此，本文设计基于边缘计算的框架，将重负载任务卸载，实现闭环实时控制。通过AirSim仿真平台及真实无人机系统验证，展示了端到端手势识别无人机控制系统的优越性。
原文摘要 · Abstract (English)
Gesture recognition presents a promising avenue for interfacing with unmanned aerial vehicles (UAVs) due to its intuitive nature and potential for precise interaction. This research conducts a comprehensive comparative analysis of vision-based hand gesture detection methodologies tailored for UAV Control. The existing gesture recognition approaches involving cropping, zooming, and color-based segmentation, do not work well for this kind of applications in dynamic conditions and suffer in performance with increasing distance and environmental noises. We propose to use a novel approach leveraging hand landmarks drawing and classification for gesture recognition based UAV control. With experimental results we show that our proposed method outperforms the other existing methods in terms of accuracy, noise resilience, and efficacy across varying distances, thus providing robust control decisions. However, implementing the deep learning based compute intensive gesture recognition algorithms on the UAV's onboard computer is significantly challenging in terms of performance. Hence, we propose to use a edge-computing based framework to offload the heavier computing tasks, thus achieving closed-loop real-time performance. With implementation over AirSim simulator as well as over a real-world UAV, we showcase the advantage of our end-to-end gesture recognition based UAV control system.
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