  This paper proposes a real-time approach for long-term inertial navigation
based only on an Inertial Measurement Unit (IMU) for self-localizing wheeled
robots. The approach builds upon two components: 1) a robust detector that uses
recurrent deep neural networks to dynamically detect a variety of situations of
interest, such as zero velocity or no lateral slip; and 2) a state-of-the-art
Kalman filter which incorporates this knowledge as pseudo-measurements for
localization. Evaluations on a publicly available car dataset demonstrates that
the proposed scheme may achieve a final precision of 20 m for a 21 km long
trajectory of a vehicle driving for over an hour, equipped with an IMU of
moderate precision (the gyro drift rate is 10 deg/h). To our knowledge, this is
the first paper which combines sophisticated deep learning techniques with
state-of-the-art filtering methods for pure inertial navigation on wheeled
vehicles and as such opens up for novel data-driven inertial navigation
techniques. Moreover, albeit taylored for IMU-only based localization, our
method may be used as a component for self-localization of wheeled robots
equipped with a more complete sensor suite.
