Diffusion Augmented Data for Inertial-Based Quadrotor Positioning

Inertial measurements play a vital role in motion tracking and navigation systems. In recent years, inertial sensing and state estimation pipelines have included deep learning algorithms to enhance their performance. The accuracy of such algorithms depends on the quality and diversity of the training dataset. Yet, the collection of large-scale inertial datasets remains a time-consuming and resource-intensive process. This challenge has limited the advancement of robust inertial sensing learning models. Recently, diffusion models have emerged as a groundbreaking class of generative models that transform artificial data generation. They have demonstrated superior performance to adversarial generative networks and other state-of-the-art techniques in handling complex tasks. In this work, we propose a diffusion-driven framework for producing synthetic inertial data applicable to inertial-based quadrotor positioning. We introduce a novel three-stage pipeline that transforms inertial signals into image representations using delay embedding. It applies vision-based diffusion models conditioned on the target regression variables, and reconstructs synthetic time-series data. To show the effectiveness of our approach, we evaluated it on the EuRoC MAV dataset. Our comprehensive cross-validation evaluation across all 11 sequences of the EuRoC MAV dataset demonstrates that incorporating synthetic inertial data during training significantly improves regression performance, reducing the displacement error by 10.6% and the average position error by 31.1%. These results confirm that our diffusion-based generative model successfully captures the complex dynamics of inertial motion patterns, enabling the creation of diverse, realistic synthetic data that reduces dependence on extensive real-world data collection while providing high-quality training augmentation for neural inertial regression networks. Our method offers a scalable solution for data-constrained quadrotor applications by reducing costly real-world data collection.