Enabling Robot-assisted Motion Capture with Human Scale Tracking Optimization
Pascal Chiu, Jiawei Huang, Yoshifumi Kitamura · 2021
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.