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

arXiv:2211.07556v2 Announce Type: replace
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 …

abstract arxiv cs.lg data devices efficiency healthcare key localization precision responsive robotics robust role supervised learning synthetic synthetic data systems technology tools tracking type vital