  While camera-based capture systems remain the gold standard for recording
human motion, learning-based tracking systems based on sparse wearable sensors
are gaining popularity. Most commonly, they use inertial sensors, whose
propensity for drift and jitter have so far limited tracking accuracy. In this
paper, we propose Ultra Inertial Poser, a novel 3D full body pose estimation
method that constrains drift and jitter in inertial tracking via inter-sensor
distances. We estimate these distances across sparse sensor setups using a
lightweight embedded tracker that augments inexpensive off-the-shelf 6D
inertial measurement units with ultra-wideband radio-based
ranging$-$dynamically and without the need for stationary reference anchors.
Our method then fuses these inter-sensor distances with the 3D states estimated
from each sensor Our graph-based machine learning model processes the 3D states
and distances to estimate a person's 3D full body pose and translation. To
train our model, we synthesize inertial measurements and distance estimates
from the motion capture database AMASS. For evaluation, we contribute a novel
motion dataset of 10 participants who performed 25 motion types, captured by 6
wearable IMU+UWB trackers and an optical motion capture system, totaling 200
minutes of synchronized sensor data (UIP-DB). Our extensive experiments show
state-of-the-art performance for our method over PIP and TIP, reducing position
error from $13.62$ to $10.65cm$ ($22\%$ better) and lowering jitter from $1.56$
to $0.055km/s^3$ (a reduction of $97\%$).
