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For decades, event cameras, which emulate the biological retina by continuously monitoring changes in light intensity, have promised to revolutionize how robots perceive the world by allowing them to react faster and operate more reliably than conventional cameras permit.

But interpreting event-camera data, which resembles a dizzying stream of points rather than a recognizable series of images, has traditionally required a patchwork of specialized algorithms, each built for a single task.

Now, engineers at the University of Pennsylvania have developed Fast Feature Field, or F³, a general-purpose computational tool to efficiently transform the tens of millions of signals that event cameras produce each second into information that many different kinds of robots can use.

The tool could transform robotics by allowing for the widespread use of event cameras in platforms like self-driving cars and drones for the first time, improving robots' ability to recognize objects, estimate distances and perceive motion in many different lighting conditions.

"We expect this to be a watershed moment in the history of event cameras, showing how to make event-camera processing reliable, fast and broadly useful," says Pratik Chaudhari, associate professor in electrical and systems engineering (ESE) with a secondary appointment in computer and information science (CIS) and senior author of a new paper in Science Robotics.

"Fast Feature Field could make the robots that increasingly fill our skies and streets much safer by helping them see the world more accurately."

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