Multirotor teams are useful 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 surroundings. Existing approaches rely on expensive or heavy perception sensors. 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. 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. As application we demonstrate that our proposed spinning camera is capable of predicting the presence of aerodynamic downwash in a challenging swapping task.

翻译：旋翼机编队在巡检、物流投送及建筑作业等任务中具有重要应用价值，此类场景要求飞行器彼此保持极近距离。在近距工况下，非线性气动效应可能引发灾难性碰撞，故需每架机器人具有环境感知能力。现有方法依赖昂贵或笨重的感知传感器，而本文提出利用旋翼机常被忽视的偏航自由度，以高角速度旋转单个低成本轻量化单目相机实现全向感知。我们提供了基于真实物理飞行与三维渲染场景构建的数据集，并在不同设置下对比两种现有学习方法在成功率、相对位置估计及下洗气流预测精度方面的性能。作为应用示范，我们验证了所提出的旋转相机在具有挑战性的换位任务中具备预测气动下洗效应的能力。