Many smartphone applications use inertial measurement units (IMUs) to sense
movement, but the use of these sensors for pedestrian localization can be
challenging due to their noise characteristics. Recent data-driven inertial
odometry approaches have demonstrated the increasing feasibility of inertial
navigation. However, they still rely upon conventional smartphone orientation
estimates that they assume to be accurate, while in fact these orientation
estimates can be a significant source of error. To address the problem of
inaccurate orientation estimates, we present a two-stage, data-driven pipeline
using a commodity smartphone that first estimates device orientations and then
estimates device position. The orientation module relies on a recurrent neural
network and Extended Kalman Filter to obtain orientation estimates that are
used to then rotate raw IMU measurements into the appropriate reference frame.
The position module then passes those measurements through another recurrent
network architecture to perform localization. Our proposed method outperforms
state-of-the-art methods in both orientation and position error on a large
dataset we constructed that contains 20 hours of pedestrian motion across 3
buildings and 15 subjects.

本论文提出了一种基于数据驱动的二阶段流程，首先估计设备的朝向，然后估计设备的位置。该流程使用普通智能手机，并依赖于循环神经网络和扩展卡尔曼滤波器来获得朝向实现定位。研究表明，我们提出的方法在定位和朝向误差方面优于现有方法。