Tech Briefs Magazine - December 2021 - 33

Compact 3D LiDAR Imaging System
A silicon chip provides a low-cost solution to help machines see the world clearly.
University of Southampton, United Kingdom
esearchers have developed a compact
3D LiDAR imaging system that
can match and exceed the performance
and accuracy of most advanced mechanical
systems currently used. 3D LiDAR
can provide accurate imaging and mapping
for many applications; for example,
it is the " eyes " for autonomous cars and
is used in facial recognition software and
by autonomous robots and drones. Ac -
curate imaging is essential for machines
to map and interact with the physical
world but the size and costs of the technology
currently needed has
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limited
LiDAR's use in commercial applications.
The new, integrated system uses silicon
photonic components and CMOS electronic
circuits in the same micro chip.
The prototype would be a low-cost solution
and could pave the way to large-volume
production of low-cost, compact,
and high-performance 3D imaging cameras
for use in robotics, autonomous navigation
systems, mapping of building
sites to increase safety, and in healthcare.
The silicon photonics system provides
much higher accuracy at distance compared
to other chip-based LiDAR systems
and most mechanical versions.
Tests of the prototype show that it has
an accuracy of 3.1 millimeters at a distance
of 75 meters.
The combination of high perfor -
mance and low-cost manufacturing could
accelerate existing applications in auton -
omy and augmented reality, as well as
open new directions such as industrial
and consumer digital twin applications
3D Hand-Sensing Wristband
The technology has possible uses in sign language translation and in monitoring disorders
that affect fine motor skills such as Parkinson's and Alzheimer's.
Cornell University, Ithaca, New York and University of Wisconsin, Madison
esearchers have designed a wristmounted
device that continuously
tracks the entire human hand in 3D. The
bracelet, called FingerTrak, can sense and
translate into 3D the many positions of
the human hand, including 20 finger
joint positions, using three or four miniature,
low-resolution thermal cameras that
read contours on the wrist. The device
could be used in sign language translation,
virtual reality, mobile health, humanrobot
interaction, and other areas.
By looking at wrist contours, the technology
could reconstruct in 3D, with
keen accuracy, where the wearer's fingers
are. The system reconstructs the
full hand posture based on the contours
of the wrist. Past wrist-mounted
cameras have been considered too
bulky and obtrusive for everyday use
and most could reconstruct only a few
discrete hand gestures.
The FingerTrak lightweight bracelet
allows for free movement. Instead of
using cameras to directly capture the
position of the fingers - the focus of
most prior research - FingerTrak uses a
combination of thermal imaging and
machine learning to virtually reconstruct
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Deep Model
Tech Briefs, December 2021
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requiring high depth accuracy, or preventive
healthcare through remote be -
havioral and vital signs monitoring re -
quiring high-velocity accuracy.
Among the problems faced by previous
integrated systems are the difficulties in
providing a dense array of pixels that can
be easily addressed; this has re stricted
them to fewer than 20 pixels whereas this
new system is the first large-scale 2D
coherent detector array consisting of 512
pixels. The researchers are working to
extend the pixel arrays and the beam
steering technology to make the system
even better suited to real-world applications
and further improve performance.
For more information, contact Peter
Franklin at P.Franklin@soton.ac.uk; +44
(07748) 321087.
The FingerTrak bracelet uses four miniature thermal cameras.
the hand. The bracelet's four miniature
thermal cameras - each about the size of
a pea - snap multiple silhouette images
to form an outline of the hand.
A deep neural network then stitches
these silhouette images together and
reconstructs the virtual hand in 3D.
Through this method, researchers were
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able to capture the entire hand pose,
even when the hand is holding an object.
The novel technical finding in this work
was discovering that the contours of the
wrist are enough to accurately predict the
entire hand posture.
For more information, contact Jeff Tyson
at jeff.tyson@cornell.edu; 607-793-5769.
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Tech Briefs Magazine - December 2021

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