DiffusionIMU: Diffusion-Based Inertial Navigation with Iterative Motion Refinement

Inertial navigation enables self-contained localization using only Inertial Measurement Units (IMUs), making it widely applicable in various domains such as navigation, augmented reality, and robotics. However, existing methods suffer from drift accumulation due to the sensor noise and difficulty capturing long-range temporal dependencies, limiting their robustness and accuracy. To address these challenges, we propose DiffusionIMU, a novel diffusion-based framework for inertial navigation. DiffusionIMU enhances direct velocity regression from IMU data through an iterative generative denoising process, progressively refining motion state estimation. It integrates the noise-adaptive feature modulation for sensor variability handling, the feature alignment mechanism for representation consistency, and the diffusion-based temporal modeling to decrease accumulated drift. Experiments show that DiffusionIMU consistently outperforms existing methods, demonstrating superior generalization to unseen users while alleviating the impact of the sensor noise.

Authors

• Xiaoqiang Teng
• Chenyang Li Lanzhou University
• Shibiao Xu School of Artificial Intelligence, Beijing University of Posts and Telecommunications
• Zhihao Hao School of Computer Science and Artificial Intelligence & Beijing Key Laboratory of Commercial Data Security Protection and Intelligent Governance, Beijing Technology and Business University, Beijing, China Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau
• Deke Guo
• Jingyuan Li Chinese Academy of Sciences
• Haisheng Li School of Computer Science and Artificial Intelligence & Beijing Key Laboratory of Commercial Data Security Protection and Intelligent Governance, Beijing Technology and Business University, Beijing, China
• Weiliang Meng MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, China School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
• Xiaopeng Zhang MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, China School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

Keywords

• Computer Vision: CV: Motion and tracking
• Robotics: ROB: Applications
• Robotics: ROB: Learning in robotics
• Robotics: ROB: Localization, mapping, state estimation