The secret role of undesired physical effects in accurate shape sensing with eccentric FBGs

TL;DR

This paper introduces a deep learning approach to improve the accuracy of eccentric fiber Bragg grating sensors for complex shape sensing by leveraging bending-induced effects usually considered noise.

Contribution

The study demonstrates that bending effects in eccentric FBGs contain valuable shape information and can be exploited using CNNs for precise shape estimation, enabling low-cost accurate sensors.

Findings

Deep learning models extract shape information from bending effects.

Eccentric FBGs can achieve accurate shape sensing with simple setups.

Bending effects enhance shape resolution in fiber sensors.

Abstract

Fiber optic shape sensors have enabled unique advances in various navigation tasks, from medical tool tracking to industrial applications. Eccentric fiber Bragg gratings (FBG) are cheap and easy-to-fabricate shape sensors that are often interrogated with simple setups. However, using low-cost interrogation systems for such intensity-based quasi-distributed sensors introduces further complications to the sensor's signal. Therefore, eccentric FBGs have not been able to accurately estimate complex multi-bend shapes. Here, we present a novel technique to overcome these limitations and provide accurate and precise shape estimation in eccentric FBG sensors. We investigate the most important bending-induced effects in curved optical fibers that are usually eliminated in intensity-based fiber sensors. These effects contain shape deformation information with a higher spatial resolution that we are now able to extract using deep learning techniques. We design a deep learning model based on a convolutional neural network that is trained to predict shapes given the sensor's spectra. We also provide a visual explanation, highlighting wavelength elements whose intensities are more relevant in making shape predictions. These findings imply that deep learning techniques benefit from the bending-induced effects that impact the desired signal in a complex manner. This is the first step toward cheap yet accurate fiber shape sensing solutions.