  Pedestrian dead reckoning is a challenging task due to the low-cost inertial
sensor error accumulation. Recent research has shown that deep learning methods
can achieve impressive performance in handling this issue. In this letter, we
propose inertial odometry using a deep learning-based velocity estimation
method. The deep neural network based on Res2Net modules and two convolutional
block attention modules is leveraged to restore the potential connection
between the horizontal velocity vector and raw inertial data from a smartphone.
Our network is trained using only fifty percent of the public inertial odometry
dataset (RoNIN) data. Then, it is validated on the RoNIN testing dataset and
another public inertial odometry dataset (OXIOD). Compared with the traditional
step-length and heading system-based algorithm, our approach decreases the
absolute translation error (ATE) by 76%-86%. In addition, compared with the
state-of-the-art deep learning method (RoNIN), our method improves its ATE by
6%-31.4%.
