AI-Powered Drone Mimics Cars and Bikes to Navigate Through City Streets | AI

A car and bicycle image datasets were used to train DroNet, a convolutional neural network that can fly a drone through the streets of a city
Photo: Robotics and Perception Group/University of Zurich

Two years ago, roboticists from Davide Scaramuzza’s lab at the University of Zurich used a set of pictures taken by cameras mounted on a hiker’s head to train a deep neural network, which was then able to fly an inexpensive drone along forest paths without running into anything. This is cool, for two reasons: The first is that you can use this technique to make drones with minimal on-board sensing and computing fully autonomous, and the second is that you can do so without collecting dedicated drone-centric training datasets first. 

In a new paper appearing in IEEE Robotics and Automation Letters, Scaramuzza and one of his Ph.D. students, Antonio Loquercio, along with collaborators Ana I. Maqueda and Carlos R. del-Blanco from Universidad Politécnica de Madrid, in Spain, present some new work in which they’ve trained a drone to autonomously fly through the streets of a city, and they’ve done it with data collected by cars and bicycles.

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