  We address the indoor localization problem, where the goal is to predict
user's trajectory from the data collected by their smartphone, using inertial
sensors such as accelerometer, gyroscope and magnetometer, as well as other
environment and network sensors such as barometer and WiFi. Our system
implements a deep learning based pedestrian dead reckoning (deep PDR) model
that provides a high-rate estimation of the relative position of the user.
Using Kalman Filter, we correct the PDR's drift using WiFi that provides a
prediction of the user's absolute position each time a WiFi scan is received.
Finally, we adjust Kalman Filter results with a map-free projection method that
takes into account the physical constraints of the environment (corridors,
doors, etc.) and projects the prediction on the possible walkable paths. We
test our pipeline on IPIN'19 Indoor Localization challenge dataset and
demonstrate that it improves the winner's results by 20\% using the challenge
evaluation protocol.
