  This paper presents the large and diverse dataset for development of
smartphone-based pedestrian navigation algorithms. This dataset consists of
about 1200 sets of inertial measurements from sensors of several smartphones.
The measurements are collected while walking through different trajectories up
to 10 minutes long. The data are accompanied by the high accuracy ground truth
collected with two foot-mounted inertial measurement units and post-processed
by the presented algorithms. The dataset suits both for training of
intellectual pedestrian navigation algorithms based on learning techniques and
for development of pedestrian navigation algorithms based on classical
approaches. The dataset is accessible at http://gartseev.ru/projects/ipin2019.
