Enabling Robot-assisted Motion Capture with Human Scale Tracking Optimization

Abstract

Motion tracking systems with viewpoint concerns or whose marker data include unreliable states have proven difficult to use despite many impactful benefits. We propose a technique inspired by active vision and using a customized hill-climbing approach to control a robot-sensor setup and apply it to a magnetic induction system capable of occlusion-free motion tracking. Our solution reduces the impact of displacement and orientation issues for markers which inherently present a dead-angle range that disturbs usability and accuracy. The resulting interface is successful in stabilizing previously unexploitable data while preventing sub-optimal states for up to hundreds of occurrences per recording and featuring an approximate 40% decrease in tracking error.