  Visual Inertial Odometry (VIO) is one of the most established state
estimation methods for mobile platforms. However, when visual tracking fails,
VIO algorithms quickly diverge due to rapid error accumulation during inertial
data integration. This error is typically modeled as a combination of additive
Gaussian noise and a slowly changing bias which evolves as a random walk. In
this work, we propose to train a neural network to learn the true bias
evolution. We implement and compare two common sequential deep learning
architectures: LSTMs and Transformers. Our approach follows from recent
learning-based inertial estimators, but, instead of learning a motion model, we
target IMU bias explicitly, which allows us to generalize to locomotion
patterns unseen in training. We show that our proposed method improves state
estimation in visually challenging situations across a wide range of motions by
quadrupedal robots, walking humans, and drones. Our experiments show an average
15% reduction in drift rate, with much larger reductions when there is total
vision failure. Importantly, we also demonstrate that models trained with one
locomotion pattern (human walking) can be applied to another (quadruped robot
trotting) without retraining.
