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. Most autonomous drones (and most autonomous robots in general) that don't navigate using a pre-existing map rely on some flavor of simultaneous localization and mapping, or (as the researchers put it), "map-localize-plan." Building a map, localizing yourself on that map, and then planning safe motion is certainly a reliable way to move around, but it requires big, complex, and of course very expensive, power-hungry sensors and computers.