用扩散模型建模惯性传感器的随机偏置，提升运动追踪精度。
Learning IMU Bias with Diffusion Model
- 将偏置建模为条件概率分布，而非直接回归
- 在真实数据上实现更贴近物理规律的预测结果
- 适合需要高精度姿态估计的机器人与可穿戴设备
惯性测量单元（IMU）在空间智能中的运动感知与追踪至关重要，但其性能受随时间变化的随机偏置影响。这些偏置受温度、振动等因素影响，具有高度复杂性，难以通过解析方法建模。近年来基于深度学习的数据驱动方法虽在偏置预测方面展现出潜力，但通常将其视为回归问题，忽略了偏置的随机特性。本文提出一种条件扩散模型，将偏置在给定IMU读数下的分布建模为概率分布，从而更准确地逼近真实偏置行为。实验表明，该方法在多个真实数据集上显著优于传统回归方法，且预测结果更符合已知的偏置演化规律。
原文摘要 · Abstract (English)
Motion sensing and tracking with IMU data is essential for spatial intelligence, which however is challenging due to the presence of time-varying stochastic bias. IMU bias is affected by various factors such as temperature and vibration, making it highly complex and difficult to model analytically. Recent data-driven approaches using deep learning have shown promise in predicting bias from IMU readings. However, these methods often treat the task as a regression problem, overlooking the stochatic nature of bias. In contrast, we model bias, conditioned on IMU readings, as a probabilistic distribution and design a conditional diffusion model to approximate this distribution. Through this approach, we achieve improved performance and make predictions that align more closely with the known behavior of bias.
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