階層学習による6自由度IMUの連続バイアス動力学の明示的モデル化とオンラインバイアス補正
Debiasing 6-DOF IMU via Hierarchical Learning of Continuous Bias Dynamics
姿勢の正解データのみを用い、ニューラル常微分方程式でIMUの連続的なバイアス動力学を明示的に学習し、オンラインでバイアスを補正する手法を提案した。
著者: Ben Liu, Tzu-Yuan Lin, Wei Zhang, Maani Ghaffari
分類: cs.RO
原文アブストラクト
This paper develops a deep learning approach to the online debiasing of IMU gyroscopes and accelerometers. Most existing methods rely on implicitly learning a bias term to compensate for raw IMU data. Explicit bias learning has recently shown its potential as a more interpretable and motion-independent alternative. However, it remains underexplored and faces challenges, particularly the need for ground truth bias data, which is rarely available. To address this, we propose a neural ordinary differential equation (NODE) framework that explicitly models continuous bias dynamics, requiring only pose ground truth, often available in datasets. This is achieved by extending the canonical NODE framework to the matrix Lie group for IMU kinematics with a hierarchical training strategy. The validation on two public datasets and one real-world experiment demonstrates significant accuracy improvements in IMU measurements, reducing errors in both pure IMU integration and visual-inertial odometry.
