  Building a complete inertial navigation system using the limited quality data
provided by current smartphones has been regarded challenging, if not
impossible. This paper shows that by careful crafting and accounting for the
weak information in the sensor samples, smartphones are capable of pure
inertial navigation. We present a probabilistic approach for orientation and
use-case free inertial odometry, which is based on double-integrating rotated
accelerations. The strength of the model is in learning additive and
multiplicative IMU biases online. We are able to track the phone position,
velocity, and pose in real-time and in a computationally lightweight fashion by
solving the inference with an extended Kalman filter. The information fusion is
completed with zero-velocity updates (if the phone remains stationary),
altitude correction from barometric pressure readings (if available), and
pseudo-updates constraining the momentary speed. We demonstrate our approach
using an iPad and iPhone in several indoor dead-reckoning applications and in a
measurement tool setup.
