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：飞行机器人团队可用于巡检、投递和建造任务，此时它们可能需要彼此极近距离飞行。在如此近距离场景中，非线性空气动力学效应可能导致灾难性碰撞，这要求每个机器人具备对周围环境的感知能力。现有方法依赖多个昂贵或沉重的感知传感器，这些感知方法因重量、算力和价格受限而难以应用于纳米多旋翼无人机。我们提出利用多旋翼常被忽视的偏航自由度，使单个廉价轻量级单目相机以高角速率旋转，实现对邻近机器人的全向感知。我们提供了基于真实物理飞行和三维渲染场景采集的数据集，并在不同设置下比较两种现有基于学习方法在成功率、相对位置估计和下洗流预测精度方面的表现。实验证明，在具有挑战性的对调任务中，所提出的旋转相机能够以超过80%的$F_1$分数预测空气动力学下洗流是否存在。