Micro-aerial vehicles dodge fast-moving objects using only onboard sensing and computation. Today’s autonomous drones have reaction times of tens of milliseconds, which is not enough for navigating fast in complex dynamic environments. To safely avoid fast moving objects, drones need low-latency sensors and algorithms. We departed from state-of-the-art approaches by using event cameras, which are bioinspired sensors with reaction times of microseconds. Our approach exploits the temporal information contained in the event stream to distinguish between static and dynamic objects and leverages a fast strategy to generate the motor commands necessary to avoid the approaching obstacles. Standard vision algorithms cannot be applied to event cameras because the output of these sensors is not images but a stream of asynchronous events that encode per-pixel intensity changes. Our resulting algorithm has an overall latency of only 3.5 milliseconds, which is sufficient for reliable detection and avoidance of fast-moving obstacles. We demonstrate the effectiveness of our approach on an autonomous quadrotor using only onboard sensing and computation. Our drone was capable of avoiding multiple obstacles of different sizes and shapes, at relative speeds up to 10 meters/second, both indoors and outdoors.

中文翻译：

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带事件摄像机的四旋翼动态避障

微型飞行器仅使用机载感应和计算来躲避快速移动的物体。如今的自主无人机具有数十毫秒的响应时间，这不足以在复杂的动态环境中快速导航。为了安全地避开快速移动的物体，无人机需要低延迟传感器和算法。我们使用事件摄影机来摆脱最先进的方法，事件摄影机是具有生物启发性的传感器，反应时间为微秒。我们的方法利用事件流中包含的时间信息来区分静态对象和动态对象，并利用一种快速策略来生成避免接近障碍所必需的运动命令。标准视觉算法无法应用于事件摄像机，因为这些传感器的输出不是图像，而是一系列异步事件的流，这些事件编码每个像素的强度变化。我们得出的算法的总延迟仅为3.5毫秒，足以进行可靠的检测并避免快速移动的障碍物。我们仅使用机载感应和计算就自主四旋翼飞机展示了我们的方法的有效性。我们的无人机能够在室内和室外以相对速度高达10米/秒的速度避免各种大小和形状的障碍物。我们仅使用机载感应和计算就自主四旋翼飞机展示了我们的方法的有效性。我们的无人机能够在室内和室外以相对速度高达10米/秒的速度避免各种大小和形状的障碍物。我们仅使用机载感应和计算就自主四旋翼飞机展示了我们的方法的有效性。我们的无人机能够在室内和室外以相对速度高达10米/秒的速度避免各种大小和形状的障碍物。