Building a complete inertial navigation system using the limited quality data provided by current smartphones has been regarded challenging, if not impossible. We present a probabilistic approach for orientation and use-case free inertial odometry, which is based on double-integrating rotated accelerations. Our approach uses a probabilistic approach in fusing the noisy sensor data and learning the model parameters online. It is able to track the phone position, velocity, and pose in real-time and in a computationally lightweight fashion. The information fusion is completed with altitude correction from barometric pressure readings (if available), zero-velocity updates (if the phone remains stationary), and pseudo-updates limiting the momentary speed. We demonstrate our approach using a standard iPad and iPhone in several indoor dead-reckoning applications and in a measurement tool setup.
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