Utilizing Synthetic Data in Supervised Learning for Robust 5-DoF Magnetic Marker Localization

TL;DR

This paper presents a neural network-based method for real-time, accurate 5-DoF magnetic marker localization that bypasses traditional computationally expensive optimization, using synthetic data generated via Finite Element Methods.

Contribution

The paper introduces a novel neural network approach for magnetic marker tracking that directly infers position and orientation, reducing computational costs and improving accuracy over traditional methods.

Findings

Achieved 4 mm positional accuracy and 8° orientation error.

Demonstrated real-time inference on a portable single-board computer.

Validated with a prototype tracking system within a compact workspace.

Abstract

Tracking passive magnetic markers plays a vital role in advancing healthcare and robotics, offering the potential to significantly improve the precision and efficiency of systems. This technology is key to developing smarter, more responsive tools and devices, such as enhanced surgical instruments, precise diagnostic tools, and robots with improved environmental interaction capabilities. However, traditionally, the tracking of magnetic markers is computationally expensive due to the requirement for iterative optimization procedures. Moreover, these methods depend on the magnetic dipole model for their optimization function, which can yield imprecise outcomes due to the model's significant inaccuracies when dealing with short distances between non-spherical magnet and sensor.Our paper introduces a novel approach that leverages neural networks to bypass these limitations, directly inferring the marker's position and orientation to accurately determine the magnet's 5 DoF in a single step without initial estimation. Although our method demands an extensive supervised training phase, we mitigate this by introducing a computationally more efficient method to generate synthetic, yet realistic data using Finite Element Methods simulations. The benefits of fast and accurate inference significantly outweigh the offline training preparation. In our evaluation, we use different cylindrical magnets, tracked with a square array of 16 sensors. We perform the sensors' reading and position inference on a portable, neural networks-oriented single-board computer, ensuring a compact setup. We benchmark our prototype against vision-based ground truth data, achieving a mean positional error of 4 mm and an orientation error of 8 degrees within a 0.2x0.2x0.15 m working volume. These results showcase our prototype's ability to balance accuracy and compactness effectively in tracking 5 DoF.