  In this work we propose a tightly-coupled Extended Kalman Filter framework
for IMU-only state estimation. Strap-down IMU measurements provide relative
state estimates based on IMU kinematic motion model. However the integration of
measurements is sensitive to sensor bias and noise, causing significant drift
within seconds. Recent research by Yan et al. (RoNIN) and Chen et al. (IONet)
showed the capability of using trained neural networks to obtain accurate 2D
displacement estimates from segments of IMU data and obtained good position
estimates from concatenating them. This paper demonstrates a network that
regresses 3D displacement estimates and its uncertainty, giving us the ability
to tightly fuse the relative state measurement into a stochastic cloning EKF to
solve for pose, velocity and sensor biases. We show that our network, trained
with pedestrian data from a headset, can produce statistically consistent
measurement and uncertainty to be used as the update step in the filter, and
the tightly-coupled system outperforms velocity integration approaches in
position estimates, and AHRS attitude filter in orientation estimates.
