Deep-learning-based high spatial resolution fiber shape sensing

Abstract Fiber optic shape sensing is an innovative technology that has enabled remarkable advances in various navigation and tracking applications. Although the state-of-the-art fiber optic shape sensing mechanisms can provide sub-millimeter spatial resolution for off-axis strain measurement and reconstruct the sensor's shape with high tip accuracy, their overall cost is very high. The major challenge in more cost-effective fiber sensor alternatives for providing accurate shape measurement is the limited sensing resolution in detecting shape deformations. Here, we present a novel data-driven technique to overcome this limitation by removing strain measurement, curvature calculation, and shape reconstruction steps. We designed a deep-learning model based on convolutional neural networks that is trained to directly predict the sensor's shape based on its spectrum. Our fiber sensor is based on easy-to-fabricate eccentric fiber Bragg gratings and can be interrogated with a simple and cost-effective readout unit in the spectral domain. We demonstrate that our deep-learning model benefits from undesired bending-induced effects (e.g., cladding mode coupling and polarization), which contain high-resolution shape deformation information. These findings are the first steps toward a low-cost yet accurate fiber shape sensing solution for detecting complex multi-bend deformations.