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Haptic feedback is essential to acquire immersive experience when interactingin virtual or augmented reality. Although the existing promising magneticlevitation (maglev) haptic system has advantages of none mechanical friction,its performance is limited by its navigation method, which mainly results fromthe challenge that it is difficult to obtain high precision, high frame rateand good stability with lightweight design at the same. In this study, wepropose to perform the visual-inertial fusion navigation based onsequence-to-sequence learning for the maglev haptic interaction. Cascade LSTMbased-increment learning method is first presented to progressively learn theincrements of the target variables. Then, two cascade LSTM networks areseparately trained for accomplishing the visual-inertial fusion navigation in aloosely-coupled mode. Additionally, we set up a maglev haptic platform as thesystem testbed. Experimental results show that the proposed cascade LSTMbased-increment learning method can achieve high-precision prediction, and ourcascade LSTM based visual-inertial fusion navigation method can reach 200Hzwhile maintaining high-precision (the mean absolute error of the position andorientation is respectively less than 1mm and 0.02{\deg})navigation for themaglev haptic interaction application.

Cascade LSTM Based Visual-Inertial Navigation for Magnetic Levitation Haptic Interaction