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DroNet: Training Drones with Data Collected from Cars

Scientists from the University of Zurich’s Are Working On DroNet, A Convolutional Neural Network That Can Safely Drive A Drone Through The Streets Of A City

Civilian drones are soon expected to be used in a wide variety of tasks, such as aerial surveillance, delivery, or monitoring of existing architectures. Nevertheless, their deployment in urban environments has so far been limited. Indeed, in unstructured and highly dynamic scenarios drones face numerous challenges to navigate autonomously in a feasible and safe way. In contrast to the traditional map-localize-plan methods, this paper explores a data-driven approach to cope with the above challenges.

To do this, scientists from the University of Zurich’s Robotics department have come up with DroNet, a convolutional neural network that can safely drive a drone through the streets of a city.

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Designed as a fast 8-layers residual network, DroNet produces, for each single input image, two outputs: a steering angle, to keep the drone navigating while avoiding obstacles, and a collision probability, to let the UAV recognize dangerous situations and promptly react to them. But how does one collect enough data in an unstructured outdoor environment, such as a city? Clearly, having an expert pilot providing training trajectories is not an option given the large amount of data required and, above all, the risk that it involves for others vehicles or pedestrians moving in the streets. Therefore, the scientist propose to train a UAV from data collected by cars and bicycles, which, already integrated into urban environments, would expose other cars and pedestrians to no danger. Although trained on city streets, from the viewpoint of urban vehicles, the navigation policy learned by DroNet is highly generalizable. Indeed, it allows a UAV to successfully fly at relative high altitudes, and even in indoor environments, such as parking lots and corridors.

DroNet is a convolutional neural network, whose purpose is to reliably drive an autonomous drone through the streets of a city. Trained with data collected by cars and bicycles, our system learns from them to follow basic traffic rules, e.g, do not go off the road, and to safely avoid other pedestrians or obstacles. Surprisingly, the policy learned by DroNet is highly generalizable, and even allows to fly a drone in indoor corridors and parking lots.

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