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Mimicking the Human Eye Can Improve Lidar

heat map of Mimicking the Human Eye to Improve Lidar
Mimicking the Human Eye to Improve Lidar

DURHAM, N.C. – Even though robots don’t have eyes with retinas, the key to helping them see and interact with the world more naturally and safely may rest in optical coherence tomography (OCT) machines commonly found in the offices of ophthalmologists. By mimicking the human eye researchers at the Pratt School of Engineering at Duke University believe we may one day have lidar-based 3D cameras.

One of the imaging technologies that many robotics companies are integrating into their sensor packages is Light Detection and Ranging, or LiDAR for short. Currently commanding great attention and investment from self-driving car developers, the approach essentially works like radar, but instead of sending out broad radio waves and looking for reflections, it uses short pulses of light from lasers.

Traditional time-of-flight LiDAR, however, has many drawbacks that make it difficult to use in many 3D vision applications. Because it requires detection of very weak reflected light signals, other LiDAR systems or even ambient sunlight can easily overwhelm the detector. It also has limited depth resolution and can take a dangerously long time to densely scan a large area such as a highway or factory floor. To tackle these challenges, researchers are turning to a form of LiDAR called frequency-modulated continuous wave (FMCW) LiDAR.

“FMCW LiDAR shares the same working principle as OCT, which the biomedical engineering field has been developing since the early 1990s,” said Ruobing Qian, a PhD student working in the laboratory of Joseph Izatt, the Michael J. Fitzpatrick Distinguished Professor of Biomedical Engineering at Duke. “But 30 years ago, nobody knew autonomous cars or robots would be a thing, so the technology focused on tissue imaging. Now, to make it useful for these other emerging fields, we need to trade in its extremely high resolution capabilities for more distance and speed.”

In a paper appearing March 29 in the journal Nature Communications, the Duke team demonstrates how a few tricks learned from their OCT research can improve on previous FMCW LiDAR data-throughput by 25 times while still achieving submillimeter depth accuracy.

OCT is the optical analogue of ultrasound, which works by sending sound waves into objects and measuring how long they take to come back. To time the light waves’ return times, OCT devices measure how much their phase has shifted compared to identical light waves that have travelled the same distance but have not interacted with another object.

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