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.
View on arXiv@article{zhou2025_2505.11763,
title={ Learning IMU Bias with Diffusion Model },
author={ Shenghao Zhou and Saimouli Katragadda and Guoquan Huang },
journal={arXiv preprint arXiv:2505.11763},
year={ 2025 }
}