Iterative Path Reconstruction for Large-Scale Inertial Navigation on Smartphones
This work addresses navigation challenges for smartphone users in urban or indoor settings where GPS signals are weak, though it is incremental as it builds on existing inertial and filtering methods.
The paper tackled the problem of accurate motion estimation on smartphones in environments with unreliable GNSS signals by using inertial measurements combined with partial GNSS data, achieving results comparable to a visual-inertial tracking scheme (Apple ARKit) in real-world tests.
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.