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.

本文提出了一种紧密耦合的扩展卡尔曼滤波器框架，使用训练有素的神经网络来回归三维位移估计和其不确定性，进而在滤波器中实现测量更新步骤，从而解决姿态、速度和传感器偏差问题。与速度积分和 AHRS 姿态滤波器方法相比，本文的系统在位置和姿态估计方面表现更好，且该网络可用于步行者数据的统计一致的测量和不确定性。