Teams of flying robots can be used for inspection, delivery, and construction tasks, in which they might be required to fly very close to each other. In such close-proximity cases, nonlinear aerodynamic effects can cause catastrophic crashes, necessitating each robots' awareness of the surrounding. Existing approaches rely on multiple, expensive or heavy perception sensors. Such perception methods are impractical to use on nano multirotors that are constrained with respect to weight, computation, and price. Instead, we propose to use the often ignored yaw degree-of-freedom of multirotors to spin a single, cheap and lightweight monocular camera at a high angular rate for omnidirectional awareness of the neighboring robots. We provide a dataset collected with real-world physical flights as well as with 3D-rendered scenes and compare two existing learning-based methods in different settings with respect to success rate, relative position estimation, and downwash prediction accuracy. We demonstrate that our proposed spinning camera is capable of predicting the presence of aerodynamic downwash with an $F_1$ score of over 80% in a challenging swapping task.

翻译：飞行机器人编队可用于检查、递送和建造任务，在这些任务中它们可能需要彼此非常接近地飞行。在如此近距离的情况下，非线性空气动力学效应可能导致灾难性碰撞，因此每个机器人必须感知周围环境。现有方法依赖于多个昂贵或笨重的感知传感器。这些感知方法不适用于受重量、计算能力和价格限制的纳米多旋翼飞行器。相反，我们提出利用多旋翼通常被忽略的偏航自由度，以高角速度旋转单个廉价轻便的单目相机，实现对邻近机器人的全向感知。我们提供了通过真实物理飞行和3D渲染场景收集的数据集，并比较了两种现有基于学习的方法在不同场景下的成功率、相对位置估计和下洗预测精度。我们证明，我们提出的旋转相机能够预测空气动力学下洗流的存在，在具有挑战性的交换任务中$F_1$分数超过80%。