AirIMU: Learning Uncertainty Propagation for Inertial Odometry

TL;DR

AirIMU introduces a hybrid data-driven and model-based approach to improve inertial odometry by accurately estimating uncertainty and non-deterministic errors, enhancing robustness across diverse platforms and IMU types.

Contribution

The paper presents AirIMU, a novel hybrid method that combines data-driven uncertainty estimation with model-based approaches for improved inertial odometry accuracy.

Findings

Achieves 31.6% accuracy improvement in pose estimation.

Effective across a wide range of IMUs and platforms.

Joint training enhances noise correction and uncertainty estimation.

Abstract

Inertial odometry (IO) using strap-down inertial measurement units (IMUs) is critical in many robotic applications where precise orientation and position tracking are essential. Prior kinematic motion model-based IO methods often use a simplified linearized IMU noise model and thus usually encounter difficulties in modeling non-deterministic errors arising from environmental disturbances and mechanical defects. In contrast, data-driven IO methods struggle to accurately model the sensor motions, often leading to generalizability and interoperability issues. To address these challenges, we present AirIMU, a hybrid approach to estimate the uncertainty, especially the non-deterministic errors, by data-driven methods and increase the generalization abilities using model-based methods. We demonstrate the adaptability of AirIMU using a full spectrum of IMUs, from low-cost automotive grades to high-end navigation grades. We also validate its effectiveness on various platforms, including hand-held devices, vehicles, and a helicopter that covers a trajectory of 262 kilometers. In the ablation study, we validate the effectiveness of our learned uncertainty in an IMU-GPS pose graph optimization experiment, achieving a 31.6\% improvement in accuracy. Experiments demonstrate that jointly training the IMU noise correction and uncertainty estimation synergistically benefits both tasks.