Using lessons learned from the eye-imaging technology optical coherence tomography (OCT), engineers have demonstrated a LiDAR system that is fast and accurate enough to potentially improve the vision of autonomous systems such as driverless cars and robotic manufacturing plants.
From: Duke University
March 29, 2022 -- 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.
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.
FMCW LiDAR takes a
similar approach with a few tweaks. The technology sends out a laser beam that
continually shifts between different frequencies. When the detector gathers
light to measure its reflection time, it can distinguish between the specific
frequency pattern and any other light source, allowing it to work in all kinds
of lighting conditions with very high speed. It then measures any phase shift
against unimpeded beams, which is a much more accurate way to determine
distance than current LiDAR systems.
"It has been very
exciting to see how the biological cell-scale imaging technology we have been
working on for decades is directly translatable for large-scale, real-time 3D
vision," Izatt said. "These are exactly the capabilities needed for
robots to see and interact with humans safely or even to replace avatars with
live 3D video in augmented reality."
Most previous work
using LiDAR has relied on rotating mirrors to scan the laser over the
landscape. While this approach works well, it is fundamentally limited by the
speed of the mechanical mirror, no matter how powerful the laser it's using.
The Duke researchers
instead use a diffraction grating that works like a prism, breaking the laser
into a rainbow of frequencies that spread out as they travel away from the
source. Because the original laser is still quickly sweeping through a range of
frequencies, this translates into sweeping the LiDAR beam much faster than a
mechanical mirror can rotate. This allows the system to quickly cover a wide
area without losing much depth or location accuracy.
While OCT devices are
used to profile microscopic structures up to several millimeters deep within an
object, robotic 3D vision systems only need to locate the surfaces of
human-scale objects. To accomplish this, the researchers narrowed the range of
frequencies used by OCT, and only looked for the peak signal generated from the
surfaces of objects. This costs the system a little bit of resolution, but with
much greater imaging range and speed than traditional LiDAR.
The result is an FMCW
LiDAR system that achieves submillimeter localization accuracy with
data-throughput 25 times greater than previous demonstrations. The results show
that the approach is fast and accurate enough to capture the details of moving
human body parts -- such as a nodding head or a clenching hand -- in real-time.
"In much the same
way that electronic cameras have become ubiquitous, our vision is to develop a
new generation of LiDAR-based 3D cameras which are fast and capable enough to
enable integration of 3D vision into all sorts of products," Izatt said.
"The world around us is 3D, so if we want robots and other automated
systems to interact with us naturally and safely, they need to be able to see
us as well as we can see them."
This research was
supported by the National Institutes of Health (EY028079), the National Science
Foundation, (CBET-1902904) and the Department of Defense CDMRP
(W81XWH-16-1-0498).
Story Source:
Materials provided
by Duke University.
Original written by Ken Kingery. Note: Content may be edited for style
and length.
https://www.sciencedaily.com/releases/2022/03/220329114712.htm
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