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.
In this paper, we take a general INS approach, without assuming legged or otherwise constrained motion, and compensate the limitations of low quality IMUs by fusing them with GNSS position fixes, which may be potentially sparse and infrequent containing large gaps in signal reception. As mentioned, there are relatively few general INS approaches for consumer-grade devices. We build upon the recent work @cite_13 , which shows relatively good path estimation results by utilizing online learning of sensor biases and manually provided loop closures or position fixes. We improve their approach in the following two ways which greatly increase the practical applicability in certain use cases: we utilize automatic GNSS based position measurements, which do not require additional manoeuvres or cooperation from the user; and we apply iterative path reconstruction methods, which provide improved accuracy in the presence of long interruptions in GNSS signal reception.
@cite_13
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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 realtime 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.