  Modern smartphones have all the sensing capabilities required for accurate
and robust navigation and tracking. In specific environments some data streams
may be absent, less reliable, or flat out wrong. In particular, the GNSS signal
can become flawed or silent inside buildings or in streets with tall buildings.
In this application paper, we aim to advance the current state-of-the-art in
motion estimation using inertial measurements in combination with partial GNSS
data on standard smartphones. We show how iterative estimation methods help
refine the positioning path estimates in retrospective use cases that can cover
both fixed-interval and fixed-lag scenarios. We compare estimation results
provided by global iterated Kalman filtering methods to those of a
visual-inertial tracking scheme (Apple ARKit). The practical applicability is
demonstrated on real-world use cases on empirical data acquired from both
smartphones and tablet devices.
