Title: Cascade LSTM Based Visual-Inertial Navigation for Magnetic Levitation Haptic Interaction,
Abstract: Haptic feedback is essential to acquire immersive experience when interacting
in virtual or augmented reality. Although the existing promising magnetic
levitation (maglev) haptic system has advantages of none mechanical friction,
its performance is limited by its navigation method, which mainly results from
the challenge that it is difficult to obtain high precision, high frame rate
and good stability with lightweight design at the same. In this study, we
propose to perform the visual-inertial fusion navigation based on
sequence-to-sequence learning for the maglev haptic interaction. Cascade LSTM
based-increment learning method is first presented to progressively learn the
increments of the target variables. Then, two cascade LSTM networks are
separately trained for accomplishing the visual-inertial fusion navigation in a
loosely-coupled mode. Additionally, we set up a maglev haptic platform as the
system testbed. Experimental results show that the proposed cascade LSTM
based-increment learning method can achieve high-precision prediction, and our
cascade LSTM based visual-inertial fusion navigation method can reach 200Hz
while maintaining high-precision (the mean absolute error of the position and
orientation is respectively less than 1mm and 0.02°)navigation for the
maglev haptic interaction application.