Hello.
This is the presentation for the following paper:
Enabling Robot-assisted Motion Capture for Human Scale Tracking Optimization
My name is Pascal Chiu and I will be presenting its contents while representing my coauthors,
Jiawei Huang and Yoshifumi Kitamura.
The context of our work is the persisting need for improved motion capture technology
, and at large, real-time 3D motion tracking.
This technology should require no introduction and has become the essential basis of almost
all current immersive interaction systems, including AR and VR.
It is also a widely adopted solution to acquire 3D motion data for various purposes from entertainment
to research.
Motion capture sensors are either placed on the tracking object itself, or in its environment.
In the second scenario, sensors located around the object are tasked to identify and track
specific features that are descriptive of the object’s motion.
This exocentric approach is common when using cameras for example, which can detect features
from markers or infer them using computer vision.
Exocentric motion capture is usually less invasive and cumbersome than self-contained
sensors like data-gloves or bodysuits with wires or batteries.
Feature-providing markers also have less electronics and are less fragile.
However, the approach of tracking from the environment creates some intrinsic problems
like expensive calibration iterations or reduced flexibility.
Across tracking technologies and use cases, we would like to make our environment sensors
more dynamic and robust against viewpoint problems that often appear in sensor-marker
setups.
A well-known viewpoint problem for motion tracking systems is occlusion, which is a
persisting challenge for camera sensors.
But in fact, there is another type of sensing technology that effectively negates this problem:
magnetic motion tracking.
Despite this advantage, there are still a number of limitations, including a smaller
tracking volume and results that depend on the relative positioning between the sensors
and the markers.
In our system, we use passive markers with a cylindric form-factor that are simple LC
circuits manufactured with a copper coil attached to a capacitor.
These markers require what we call a driving coil to activate, and their positional data
is inferred from information captured through an array of sensing coils.
Specifically, each marker has a resonant frequency, which is used to modulate an AC current that
we input into the driving coil.
This generates an induction process and allows us to resonate with several markers with a
single input current.
Each resonating LC marker contributes its own fluctuating magnetic field to the overall
electromagnetic environment.
By distributing an array of sensing coils over the range of this overall magnetic field,
we can measure the induction-produced current from the sensing coils and infer each marker’s
location and inclination.
This principle inherently relies on the respective inclination between the markers and the driving
coil’s magnetic field, or the sensing coils and the marker’s magnetic field.
In each case, the electromotive force required to resonate with the markers and the sensing
coils depends on the magnetic flux traversing the markers.
This flux becomes minimal or negligible when markers are placed parallel to our sensor
setup along any horizontal direction.
We call this the dead-angle problem for simplicity.
When markers produce no contribution, the system cannot infer their position and the
deep neural-network that we use produces noise as a result.
In this example, markers placed on the hand’s fingers are in a dead-angle state when using
a half-grip gesture.
As seen in the video, we can simply address this issue by changing the sensors’ and
driving coil’s orientation.
This effectively translates into a viewpoint problem.
In this work, we tried to solve this problem from its origin, which is the inclination
between our sensors and markers.
By actuating the sensors, we propose to implement an optimization problem and allow the system
to correct this relative inclination.
The idea is to align the sensor array with the marker set’s centroid with a robotic
apparatus.
This requires a control algorithm, which is based on a PID controller and a dampening
system integrated in the robotic arm’s control system.
On top of controlling the position of the sensor array, we add a series of additional
parameters to address various issues.
To reduce unwanted robot motions induced by small marker displacement or noise, a high-pass
threshold is applied.
We also divide the tracking volume into different areas and allow the robot to overshoot motion
corrections when markers are close to the array’s edge.
The viewpoint optimizer is based on a steepest-ascent hill climbing model.
We maximize the displayed Gamma function, which is simply the product of every marker’s
axle projected on the normal direction of the sensor array (described as G).
The system explores the optimization space by controlling angles Theta and Phi, and receives
Gamma scaled by a potential constant k as reward.
In addition, we apply an exponential weight w, which varies from marker to marker.
This weight is composed with an angle weight and/or a speed weight.
The angle weight prioritizes markers closer to dead-angles, while the speed weight prioritizes
markers that rotate slowly.
This is because fast rotating markers produce short dead-angles that can be filtered in
the output signal.
Our hypotheses are based on the performance of each of those weights, and we evaluate
the four conditions: ANGLE, ANGLE + SPEED, SPEED, and NO WEIGHT.
Samples are recorded with hand gesture motions, several times for each condition, and we perform
a statistical distribution analysis on a score metric.
The definition of the score is the number of dead-angles that only exist in the dynamic
array’s case, minus the number of dead-angles that only exist with a static sensor setup.
The score values show that using angle weights result in improved performance, while speed
weights show no significant change.
We confirmed similar tendencies when comparing weights with natural tasks instead of repeated
gestures.
Results found with repeated gestures are indeed to be taken with a pinch of salt since the
performance of the system depends on how challenging the current motion is.
Lastly, we validate the overall accuracy of the system’s output under the same conditions
as the baseline system, here labelled as the Static condition.
These results however, were acquired for a single marker.
To summarize, our solution showed promising results for the following problem: finding
an optimal viewing angle for a set of target that are described each instant as vector
directions with a reliability gradient.
This allowed us to provide a dynamic viewpoint solution to our motion tracking system.
The major limitation to this approach is that it does not work alone.
Fast marker rotations are delegated to a software filter and our speed weight was designed with
this premise.
Second, the optimal viewpoint may not exist in some configurations, or be unreachable
due to mechanical constraints.
Our approach was designed to have its weaknesses compensated by the previous filter solution,
while addressing remaining issues with its strengths.
Lastly, the absence of environmental barriers or occlusion also simplifies viewpoint problems
considerably.
As long as markers are within range, we can always get a continuous reading of their reliability.
This concludes our presentation.
Thank you very much for your attention and interest.
You can find additional implementation details in both the paper and supplement video.