A Simple Self-Supervised IMU Denoising Method for Inertial Aided Navigation

Inertial Measurement Unit (IMU) plays an important role in inertial aided navigation on robots. However, raw IMU data could be noisy, especially for low-cost IMUs, and thus requires efficient pre-processing or denoising before applying further navigation algorithms. Conventional IMU denoising approaches are mostly hand-crafted and may face concerns such as sensor modelling errors and generalization issues. Several recent works leverage deep neural networks (DNNs) to tackle this problem and achieve promising results. However, currently reported deep learning methods are based on supervised learning, requiring sufficient and accurate annotations. While in real-world applications, such annotations can be expensive or unavailable, making these methods not practical. To address the above research gap, we propose incorporating self-supervised learning and future-aware inference for IMU denoising. The end-to-end navigation evaluation results on EuRoC and TUM-VI datasets are promising. The code will be publicly available at https://github.com/KleinYuan/IMUDB.