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.
|Title of host publication||Proceedings of the International Conference on Information Fusion (FUSION)|
|Publication status||Published - Feb 2020|
|MoE publication type||A4 Article in a conference publication|
|Event||International Conference on Information Fusion - Ottawa, Canada|
Duration: 2 Jul 2019 → 5 Jul 2019
|Conference||International Conference on Information Fusion|
|Period||02/07/2019 → 05/07/2019|
Cortes Reina, S., Hou, Y., Kannala, J., & Solin, A. (2020). Iterative path reconstruction for large-scale inertial navigation on smartphones. In Proceedings of the International Conference on Information Fusion (FUSION) IEEE.