Autonomous swarms of multi-Unmanned Aerial Vehicle (UAV) system requires an accurate and fast relative state estimation. Although monocular frame-based camera methods perform well in ideal conditions, they are slow, suffer scale ambiguity, and often struggle in visually challenging conditions. The advent of event cameras addresses these challenging tasks by providing low latency, high dynamic range, and microsecond-level temporal resolution. This paper proposes a framework for relative state estimation for quadrotors using event-based propeller sensing. The propellers in the event stream are tracked by detection to extract the region-of-interests. The event streams in these regions are processed in temporal chunks to estimate per-propeller frequencies. These frequency measurements drive a kinematic state estimation module as a thrust input, while camera-derived position measurements provide the update step. Additionally, we use geometric primitives derived from event streams to estimate the orientation of the quadrotor by fitting an ellipse over a propeller and backprojecting it to recover body-frame tilt-axis. The existing event-based approaches for quadrotor state estimation use the propeller frequency in simulated flight sequences. Our approach estimates the propeller frequency under 3% error on a test dataset of five real-world outdoor flight sequences, providing a method for decentralized relative localization for multi-robot systems using event camera.

翻译：多无人机（UAV）系统的自主集群需要准确且快速的相对状态估计。尽管基于单目帧相机的标方法在理想条件下表现良好，但速度慢、存在尺度模糊性，且常在视觉条件复杂时失效。事件相机的出现通过提供低延迟、高动态范围和微秒级时间分辨率解决了这些挑战性任务。本文提出了一种基于事件螺旋桨感知的四旋翼相对状态估计框架。通过对事件流中的螺旋桨进行检测跟踪，提取感兴趣区域。这些区域中的事件流按时间块进行处理，以估计每个螺旋桨的频率。这些频率测量值作为推力输入驱动运动学状态估计模块，而相机获取的位置测量值提供更新步骤。此外，我们利用事件流导出的几何基元，通过拟合螺旋桨椭圆并反投影以恢复机体坐标系倾斜轴，来估计四旋翼姿态。现有基于事件的四旋翼状态估计方法仅在模拟飞行序列中使用螺旋桨频率。我们的方法在包含五个真实户外飞行序列的测试数据集上，将螺旋桨频率估计误差控制在3%以下，为多机器人系统提供了一种基于事件相机的分散式相对定位方法。