RuDaCoP: The Dataset for Smartphone-based Intellectual Pedestrian Navigation
This dataset addresses the need for diverse and accurate data for smartphone-based pedestrian navigation research, though it is incremental as it builds on existing data collection methods.
The authors introduced RuDaCoP, a large dataset of about 1200 sets of inertial measurements from smartphones for pedestrian navigation, accompanied by high-accuracy ground truth from foot-mounted sensors, to support both learning-based and classical algorithm development.
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.