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In this work, we propose a novel method for performing inertial aidednavigation, by using deep neural networks (DNNs). To date, most DNN inertialnavigation methods focus on the task of inertial odometry, by taking gyroscopeand accelerometer readings as input and regressing for integrated IMU poses(i.e., position and orientation). While this design has been successfullyapplied on a number of applications, it is not of theoretical performanceguarantee unless patterned motion is involved. This inevitably leads tosignificantly reduced accuracy and robustness in certain use cases. To solvethis problem, we design a framework to compute observable IMU integration termsusing DNNs, followed by the numerical pose integration and sensor fusion toachieve the performance gain. Specifically, we perform detailed analysis on themotion terms in IMU kinematic equations, propose a dedicated network design,loss functions, and training strategies for the IMU data processing, andconduct extensive experiments. The results show that our method is generallyapplicable and outperforms both traditional and DNN methods by wide margins.

IMU Data Processing For Inertial Aided Navigation: A Recurrent Neural Network Based Approach