Uploaded October 2019 | Updated September 2026, 3 weeks ago

---

"Preintegrated Velocity Bias Estimation to Overcome Contact Nonlinearities in Legged Robot Odometry"
David Wisth, Marco Camurri, Maurice Fallon

Paper link: ieeexplore.ieee.org/document/9197214
VILENS website: ori.ox.ac.uk/labs/drs/vilens-tightly-fused-multi-sensor-odometry

Abstract:
In this paper, we present a novel factor graph
formulation to estimate the pose and velocity of a quadruped
robot on slippery and deformable terrains. The factor graph
includes a new type of preintegrated velocity factor that incor-
porates velocity inputs from leg odometry. To accommodate
for leg odometry drift, we extend the robot’s state vector
with a bias term for this preintegrated velocity factor. This
term incorporates all the effects of unmodeled uncertainties
at the contact point, such as slippery or deformable grounds
and leg flexibility. The bias term can be accurately estimated
thanks to the tight fusion of the preintegrated velocity factor
with stereo vision and IMU factors, without which it would
be unobservable. The system has been validated on several
scenarios that involve dynamic motions of the ANYmal robot on
loose rocks, slopes and muddy ground. We demonstrate a 26%
improvement of relative pose error compared to our previous
work and 52% compared to a state-of-the-art proprioceptive
state estimator.