Overcoming the Intrinsic Performance Limitations of MEMS IMU via Diffusion-Based Generative Learning
For navigation and localization systems relying on low-cost IMUs, this method offers a way to achieve performance comparable to high-grade IMUs without hardware upgrades.
The paper proposes a diffusion-based generative learning framework to synthesize high-fidelity virtual IMU data from low-cost IMU measurements, overcoming hardware limitations. Experimental results show significant improvements in positioning and attitude estimation, with thinner and more consistent point clouds in airborne mapping.
Inertial measurement units (IMUs) are fundamental sensing components in multi-source integrated navigation systems, and their performance directly determines the accuracy and reliability of solutions. However, the precision of low-cost IMUs is inherently constrained by hardware limitations. Recently, generative artificial intelligence has demonstrated remarkable capability in modeling complex data distributions and reconstructing high-fidelity signals. Motivated by this, we propose a diffusion-based generative learning framework for synthesizing high-fidelity virtual IMU data from low-cost IMU measurements. Specifically, a conditional diffusion model based on a U-Net architecture is constructed, where high-grade IMU measurements are utilized as ground-truth priors and low-cost IMU measurements are employed as conditional inputs. The virtual IMU data generated by the model is used for subsequent navigation and localization tasks. Experimental results demonstrate that the generated virtual IMU data significantly outperform the original low-cost IMU measurements in both positioning and attitude estimation. Furthermore, we transfer the model to airborne mapping experiments, where the proposed method produces thinner and more consistent point clouds. Overall, the proposed framework breaks the performance limits of low-cost IMU and demonstrates the potential of diffusion-based generative learning for virtual high-grade IMU data.