Tech Briefs Magazine - June 2022 - 41

RF-Grasp uses both a camera and an
RF reader to find and grab tagged objects,
even when they're fully blocked
from the camera's view. It consists of a
robotic arm attached to a grasping hand.
The camera sits on the robot's wrist.
The RF reader stands independent of
the robot and relays tracking information
to the robot's control algorithm.
So, the robot is constantly collecting
both RF tracking data and a visual picture
of its surroundings.
Integrating these two data streams into
the robot's decision making was one of
the biggest challenges the researchers
faced. The robot has to decide, at each
point in time, which of the streams is
more important to think about. The robot
initiates the seek-and-pluck pro cess by
pinging the target object's RF tag for a
sense of its whereabouts. The sequence is
akin to hearing a siren from behind, then
turning to look and get a clearer picture
of the siren's source.
With its two complementary senses, RFGrasp
zeroes in on the target object. As it
gets closer and even starts manipulating
the item, vision, which provides much finer
detail than RF, dominates the robot's
decision making.
Compared to a similar robot that was
equipped with only a camera, RF-Grasp
was able to pinpoint and grab its target
object with about half as much total
movement. Plus, RF-Grasp displayed
the unique ability to " declutter " its environment
- removing packing materials
and other obstacles in its way in order
to access the target.
RF-Grasp's RF sensing could instantly
verify an item's identity in a warehouse
without the need to manipulate the
item, expose its barcode, then scan it.
Potential home applications including
locating the right wrench from a toolbox
or locating lost items.
For more information, contact Abby
Abazorius at abbya@mit.edu; 617-253-2709.
Filter Enhances Robot Vision on 6D Pose Estimation
The filter gives robots greater spatial perception so they can manipulate objects and navigate
through space more accurately.
University of Illinois at Urbana-Champaign, Urbana, IL
R
obots are good at making identical
repetitive movements such as a simple
task on an assembly line. But they
lack the ability to perceive objects as
they move through an environment. A
recent study was conducted by researchers
at the University of Illinois at
Urbana-Champaign, NVIDIA, the University
of Washington, and Stanford
University on 6D object pose estimat
PoseRBPF
tion
to develop a filter to give robots
greater spatial perception, so they can
manipulate objects and navigate through
space more accurately.
While 3D pose provides location information
on X, Y, and Z axes - relative location
of the object with respect to the
camera - 6D pose gives a much more
complete picture. Much like describing
an airplane in flight, the robot needs to
translation
orientation
distribution
t + 1
know the three dimensions of the object's
orientation: yaw, pitch, and roll. In real-life
environments, all six of those dimensions
are constantly changing.
The filter was developed to help robots
analyze spatial data. The filter looks at
each particle, or piece of image information
collected by cameras aimed at an object
to help reduce judgement errors.
In an image-based 6D pose estimation
framework, a particle filter uses samples
to estimate the position and orientation.
Every particle is like a hypothesis - a
guess about the position and orientation
that requires estimation. The particle filter
uses observation to compute the value
of importance of the information from
the other particles and eliminates the incorrect
estimations.
Previously, there was no system to estimate
the full distribution of the orientation
of the object. This gives important
uncertainty
information
for robot
t + m
Overview of the PoseRBPF framework for 6D object pose tracking. The method leverages a
Rao-Blackwellized particle filter and an auto-encoder network to estimate the 3D translation and a
full distribution of the 3D rotation of a target object from a video sequence. (Image: UIUC)
Tech Briefs, June 2022
www.techbriefs.com
manip ulation. The new filter uses 6D object
pose tracking in the Rao-Blackwellized
particle filtering framework, where
the 3D rotation and the 3D translation
of an object are separated. This allows
the new approach, called PoseRBPF, to
efficiently estimate the 3D translation of
an object along with the full distribution
over the 3D rotation. As a result, PoseRBPF
can track objects with arbitrary
symmetries while still maintaining adequate
posterior distributions.
For more information, contact Lois Yoksoulian
at leyok@illinois.edu; 217-244-2788.
41
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Tech Briefs Magazine - June 2022

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