Deep IMU Bias Inference for Robust Visual-Inertial Odometry with Factor Graphs

TL;DR

This paper introduces a neural network-based method to learn IMU bias evolution, improving visual-inertial odometry robustness across various locomotion patterns and visual conditions, especially during visual tracking failures.

Contribution

It proposes training neural networks, specifically LSTMs and Transformers, to explicitly model IMU bias evolution, enhancing odometry accuracy and generalization across different motion types.

Findings

15% average reduction in drift rate

Significant improvements during total vision failure

Models trained on one locomotion pattern generalize to others

Abstract

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.