用神经微分方程显式建模惯性传感器漂移，无需真实偏置数据即可提升定位精度。
Debiasing 6-DOF IMU via Hierarchical Learning of Continuous Bias Dynamics
- 提出基于矩阵李群的神经微分方程框架，显式学习连续偏置动态
- 仅需姿态真值，在两个公开数据集和真实实验中显著降低积分误差
- 适合需要高精度惯性导航的机器人、AR/VR等实时系统使用
本文提出一种深度学习方法，用于在线消除惯性测量单元（IMU）陀螺仪与加速度计的偏置。现有方法多隐式学习偏置项，而显式偏置学习更具可解释性且与运动无关，但因缺乏真实偏置数据而未被充分探索。为此，我们设计了一种神经微分方程（NODE）框架，显式建模连续偏置动态，仅需姿态真值——该数据在多数数据集中均可获得。通过将标准NODE扩展至用于IMU运动学的矩阵李群，并采用分层训练策略实现。在两个公开数据集和一次真实实验中的验证表明，该方法显著提升了IMU测量精度，降低了纯IMU积分及视觉-惯性里程计中的误差。
原文摘要 · Abstract (English)
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.
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