Inertial measurement units (IMU), usually composed of accelerometers and gyroscopes; are well known to have inherent errors in their data, originating from bias, random walk noise, temperature dependence etc. creating a highly non-linear dependence. Typically, extended Kalman filters are used to estimate and remove these errors for stable measurement of orientations and angular velocities: but even this is not entirely accurate, as some higher order errors are ignored or approximated, and the fact that the Markov assumption ignores the effect of values older than the immediately previous value while predicting a future value. The highest levels of accuracy in IMUs are usually obtained after a rigorous factory calibration (which, in turn, makes the good ones very expensive).
In a scenario like this, how applicable would recurrent neural networks be in modeling these errors? Assuming my 'training data' consists of the accelerometer and gyroscope values, which can be fused to obtain a noisy orientation estimate, and at the same time, a much more precise orientation estimate coming from another sensor (for example, a very accurate GPS): would it be possible to replace the functionality of the Kalman filter by an RNN for error estimation and sensor output prediction?