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The precise orientation estimation of moving objects in 3D space is crucial for the inertial and magnetic measurement unit (IMMU)-based motion capture applications. Disturbance components such as external acceleration and magnetic disturbance in the sensor signal deteriorate the estimation accuracy. While conventional filters such as Kalman filters and complementary filters successfully deal with these issues, there are still much room to improve the estimation performance. One alternative approach involves training an end-to-end neural network (NN) using raw IMMU datasets based on ground truth measurements. In this study, we propose an end-to-end NN to estimate a 3D orientation over time without an additional conventional filter. The architecture of the proposed NN comprises two separate recurrent NNs in a parallel configuration. The proposed parallel network can independently estimate two vectors, corresponding to the attitude and heading, and then combine the two vectors to form a direction cosine matrix (DCM) that represents the 3D orientation. The proposed DCM-based NN has been experimentally verified under various disturbance conditions. The orientation estimation accuracy of the proposed method was superior to those of the conventional filters as well as that of the quaternion-based NN.

A Parallel Recurrent Neural Network for Robust Inertial and Magnetic Sensor-based 3D Orientation Estimation