用事件流实时追踪螺旋桨并估算转速，精度达微秒级。
HelixTrack: Event-Based Tracking and RPM Estimation of Propeller-like Objects
- 基于事件流构建全事件驱动模型，通过动态单应变换对齐旋翼平面。
- 联合估计相位与姿态，实现微秒级延迟的稳定追踪和转速测量。
- 适用于无人机安全感知，尤其适合高速旋转目标在强运动干扰下检测。
无人机与旋转机械的安全感知需要在自身运动和强干扰下，以微秒级延迟追踪快速周期性运动。传统帧基与事件基追踪器因周期性特征违反平滑运动假设而易漂移或失效。本文提出HelixTrack，一种全事件驱动方法，可同时追踪螺旋桨类物体并估计其每分钟转数（RPM）。通过在线估计的单应性将输入事件从图像平面回映到旋翼平面；卡尔曼滤波器实时维护相位估计；批处理迭代更新通过耦合相位残差与几何信息优化物体姿态。目前尚无公开数据集专门针对螺旋桨类物体的联合追踪与转速估计。为此，我们构建了包含13个高分辨率事件序列的TQE数据集，共52个旋转物体，采集距离为2米和4米，涵盖递增的自身运动，并提供微秒级真实转速标签。在TQE上，HelixTrack以约11.8倍实时速度处理全速率事件，响应延迟低于实时，且显著优于适配后的逐事件与聚合基线方法。
原文摘要 · Abstract (English)
Safety-critical perception for unmanned aerial vehicles and rotating machinery requires microsecond-latency tracking of fast, periodic motion under egomotion and strong distractors. Frame-based and event-based trackers drift or break on propellers because periodic signatures violate their smooth-motion assumptions. We tackle this gap with HelixTrack, a fully event-driven method that jointly tracks propeller-like objects and estimates their rotations per minute (RPM). Incoming events are back-warped from the image plane into the rotor plane via a homography estimated on the fly. A Kalman Filter maintains instantaneous estimates of phase. Batched iterative updates refine the object pose by coupling phase residuals to geometry. To our knowledge, no public dataset targets joint tracking and RPM estimation of propeller-like objects. We therefore introduce the Timestamped Quadcopter with Egomotion (TQE) dataset with 13 high-resolution event sequences, containing 52 rotating objects in total, captured at distances of 2 m / 4 m, with increasing egomotion and microsecond RPM ground truth. On TQE, HelixTrack processes full-rate events (approx. 11.8x real time) faster than real time and microsecond latency. It consistently outperforms per-event and aggregation-based baselines adapted for RPM estimation.
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