Im6d

Abstract

We propose IM6D, a novel real-time magnetic motion-tracking system using multiple identifiable, tiny, lightweight, wireless and occlusion-free markers. It provides reasonable accuracy and update rates and an appropriate working space for dexterous 3D interaction. Our system follows a novel electromagnetic induction principle to externally excite wireless LC coils and uses an externally located pickup coil array to track each of the LC coils with 5-DOF. We apply this principle to design a practical motion-tracking system using multiple markers with 6-DOF and to achieve reliable tracking with reasonable speed. We also solved the principle's inherent dead-angle problem. Based on this method, we simulated the configuration of parameters for designing a system with scalability for dexterous 3D motion. We implemented an actual system and applied a parallel computation structure to increase the tracking speed. We also built some examples to show how well our system works for actual situations.

Reconstruction of Dexterous 3D Motion Data from a Flexible Magnetic Sensor with Deep Learning and Structure-Aware Filtering

“…To address power source limitations, different methods have been proposed to track induced coils (e.g., [46]). A system based on this idea with multiple tiny, identifiable, lightweight, wireless, and occlusionfree markers [15,16] is very promising for such dexterous 3D motiontracking. It uses passively induced inductor-capacitor (LC) coils as markers, embeds the flux sensors in the environment, and permits markers that are small enough for users to track the movements of very small subjects.…”

“…Using a set of three LC coils as a single marker was proposed to solve the dead-angle problem and to allow 6-DOF measurements. Unfortunately, each marker becomes too bulky, and the maximum number of markers was reduced to 1/3 [15]. Regarding initialization, the system's numerical solver relies on temporal coherence, which often fails when the marker is lost.…”

“…The magnetic motion-tracking system based on electromagnetic induction [15,16] is promising; however, when applied to practical use, limitations arise in several factors: initialization, speed, dead angles, and layout flexibility. These limitations cause frequent tracking loss, where recovery is difficult [16].…”

“…These limitations cause frequent tracking loss, where recovery is difficult [16]. Another work solved the major deadangle problem, but the trade-offs, including larger markers and fewer markers for simultaneous use, are sometimes unacceptable [15].…”

“…In this paper, we propose a reconstruction method for dexterous 3D motion data based on the same tracking principle of a previous work [15,16] but with completely new computational approaches: deep learning and structure-aware filtering. Our key insight is using a deep-learning approach instead of a numerical implicit solver to track markers and a novel structure-aware temporal bilateral filter to further reconstruct smooth, accurate 3D motion.…”