用螺旋桨转速感知无人机，实现毫秒级高精度追踪
Count Every Rotation and Every Rotation Counts: Exploring Drone Dynamics via Propeller Sensing
- 通过事件相机捕捉螺旋桨转速，抗环境噪声干扰
- 转速误差仅0.23%，飞行指令识别准确率达96.5%
- 适合需要实时低延迟感知的无人机监控场景
随着无人机应用日益普及，地面非接触式感知空中无人机变得至关重要。本文提出基于事件相机的系统 extit{EventPro}，通过聚焦螺旋桨旋转速度显著提升感知性能。该系统包含两个核心模块： extit{Count Every Rotation} 利用事件相机特性，有效抑制环境噪声影响，实现实时精准的螺旋桨转速估计； extit{Every Rotation Counts} 则利用这些速度信息推断无人机内外部运动状态。在真实无人机配送场景下的大量测试表明， extit{EventPro} 的感知延迟仅为3ms，转速估计误差低至0.23%；同时可实现96.5%精度的飞行指令识别，并在融合其他感知模态时使追踪精度提升超过22%。
原文摘要 · Abstract (English)
As drone-based applications proliferate, paramount contactless sensing of airborne drones from the ground becomes indispensable. This work demonstrates concentrating on propeller rotational speed will substantially improve drone sensing performance and proposes an event-camera-based solution, \sysname. \sysname features two components: \textit{Count Every Rotation} achieves accurate, real-time propeller speed estimation by mitigating ultra-high sensitivity of event cameras to environmental noise. \textit{Every Rotation Counts} leverages these speeds to infer both internal and external drone dynamics. Extensive evaluations in real-world drone delivery scenarios show that \sysname achieves a sensing latency of 3$ms$ and a rotational speed estimation error of merely 0.23\%. Additionally, \sysname infers drone flight commands with 96.5\% precision and improves drone tracking accuracy by over 22\% when combined with other sensing modalities. \textit{ Demo: {\color{blue}https://eventpro25.github.io/EventPro/.} }
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