3D‐Shape Sensing via Monte Carlo‐Optimized Spatial Resolution in Multi‐Channel Parallel OFDR
Kaijun Liu, Xin Wang, Guolu Yin, Tao Zhu
Abstract
Abstract Recent advances in minimally invasive surgical robotics and aerospace structural health monitoring have highlighted the critical need for optical fiber shape sensing systems. Current fiber‐optic shape sensing implementations have basic limits in single‐shot parallel measurement capabilities and further exploration into comprehensive error analysis frameworks. This study demonstrates a frequency‐spatial division multiplexed optical frequency domain reflectometry (FSDM‐OFDR) system that allows for parallel interrogation of simultaneous multi‐channel measurements. Based on this system, a shape reconstruction algorithm with an external twist compensation and a self‐helical compensation is proposed, which improves accuracy 8.6 times compared to without torsion compensation. Furthermore, a Monte Carlo‐based statistical model is proposed to demonstrate a spatial resolution optimization strategy for minimizing reconstruction error. Finally, 0.2% and 0.63% reconstruction errors are achieved for 2D/3D geometries at 657 µm spatial resolution, and the accuracy of shape reconstruction is further improved by 2.3 times. This technological advancement has set a new performance standard for smart medical devices and infrastructure monitoring systems.