Non-linear compensation of production inaccuracies and material drift by adjusting the sensor data fusion algorithms for shape sensing based on FBG-optical fibers

Kalman-Filter-Based, Dynamic 3-D Shape Reconstruction for Steerable Needles With Fiber Bragg Gratings in Multicore Fibers

“…Similar to single-core fibers [25], [46], imprecise placement of the MCFs along the needle may be another possible source of error. Errors can also be attributed to calibration inaccuracies because, as in [45], small errors in wavelength in zerostrain and bending direction with respect to the 1 st core, θ, accumulate throughout the MCF length, resulting in significant position error at the tip.…”

“…Shape reconstruction methods based on fiber Bragg grating (FBG)-inscribed optical fibers have been introduced for about a decade [20], [21], [22], [23]. Particularly, improvements arising with the use of multi-core optical fibers (MCFs) [24] have enabled the spread of FBG-based shape reconstruction methods because they eliminate the positioning and alignment difficulties encountered with single-core fibers [25]. FBGbased reconstruction methods in the literature can be grouped into 5 main categories: Frenet-Serret frame-based methods [26], [27], parallel transport frame-based methods [28], [24], piecewise constant curvature methods [22], [29], polynomial shape-based methods [30], [31], [20] and data-driven regression approaches (DDRA) [32].…”

D2.1 - Pseudo-Data Generation Software for FBG-Optical Fiber Based Shape Sensing

“…[2], [3], [4], [5], [6], [7], [8], [4] The Institute for Anthropomatics and Robotics -Intelligent Process Control and Robotics -Karlsruhe Institute of Technology (IAR-IPR-KIT) is doing research on shape sensing since 2011. The standard algorithms for shape reconstruction are implemented and various prototypes of different material are build to test the main functionalities of the sensor system [9], [10], [11], [12]. One prototype is shown in Fig.…”