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Teams of flying robots can be used for inspection, delivery, and constructiontasks, in which they might be required to fly very close to each other. In suchclose-proximity cases, nonlinear aerodynamic effects can cause catastrophiccrashes, necessitating each robots' awareness of the surrounding. Existingapproaches rely on multiple, expensive or heavy perception sensors. Suchperception methods are impractical to use on nano multirotors that areconstrained with respect to weight, computation, and price. Instead, we proposeto use the often ignored yaw degree-of-freedom of multirotors to spin a single,cheap and lightweight monocular camera at a high angular rate foromnidirectional awareness of the neighboring robots. We provide a datasetcollected with real-world physical flights as well as with 3D-rendered scenesand compare two existing learning-based methods in different settings withrespect to success rate, relative position estimation, and downwash predictionaccuracy. We demonstrate that our proposed spinning camera is capable ofpredicting the presence of aerodynamic downwash with an $F_1$ score of over 80%in a challenging swapping task.

Virtual Omnidirectional Perception for Downwash Prediction within a Team of Nano Multirotors Flying in Close Proximity