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

Haptic feedback is crucial to immersive experience in virtual and augmented reality applications. The existing promising maglev haptic devices have advantages of no mechanical friction and low inertia. However, their performance is limited by the navigation approach, which mainly results from the challenge that it is difficult to obtain high precision, high frequency, and good stability with lightweight design at the same time. In this study, we reformulate visual-inertial navigation as a regression problem, and adopt deep learning to perform fusion navigation for maglev haptic interaction. A cascade-LSTM-based q-increment learning method is first proposed to progressively learn the increments of target variables. Two cascade LSTM networks are then constructed to estimate the increments of position and orientation, which are pipelined to accomplish visual-inertial fusion navigation. Additionally, we set up a maglev haptic platform as the system testbed. Experimental results show that our cascade-LSTMbased visual-inertial fusion navigation approach can reach 200 Hz while maintaining high-precision navigation (the mean absolute error of the position and orientation is less than 1 mm and 0.02°, respectively) for a maglev haptic interactive deformation application.