仅用事件相机数据，通过螺旋桨转速预测无人机轨迹。
Event-Only Drone Trajectory Forecasting with RPM-Modulated Kalman Filtering
- 从原始事件数据中提取螺旋桨转速，融合到感知转速的卡尔曼滤波器中。
- 在0.4秒和0.8秒预测时长下，平均误差与最终误差均优于学习方法和基础卡尔曼滤波。
- 无需RGB图像或训练数据，适合高速动态场景下的实时轨迹预测。
事件相机提供高时间分辨率视觉感知，非常适合观测快速移动的空中物体；然而，其在无人机轨迹预测中的应用仍受限。本文提出一种仅依赖事件数据的无人机轨迹预测方法，利用螺旋桨运动引起的视觉线索。直接从原始事件数据中提取螺旋桨转速，并融合进一个感知转速的卡尔曼滤波框架中。在FRED数据集上的评估显示，该方法在0.4秒和0.8秒预测时长下，平均距离误差与最终距离误差均优于基于学习的方法和基础卡尔曼滤波器。结果表明，该方法可在不依赖RGB图像或训练数据的情况下，实现鲁棒且精确的短中期轨迹预测。
原文摘要 · Abstract (English)
Event cameras provide high-temporal-resolution visual sensing that is well suited for observing fast-moving aerial objects; however, their use for drone trajectory prediction remains limited. This work introduces an event-only drone forecasting method that exploits propeller-induced motion cues. Propeller rotational speed are extracted directly from raw event data and fused within an RPM-aware Kalman filtering framework. Evaluations on the FRED dataset show that the proposed method outperforms learning-based approaches and vanilla kalman filter in terms of average distance error and final distance error at 0.4s and 0.8s forecasting horizons. The results demonstrate robust and accurate short- and medium-horizon trajectory forecasting without reliance on RGB imagery or training data.
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