Inertial Odometry on Handheld Smartphones

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Journal Title
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Conference article in proceedings
Date
2018
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Language
en
Pages
1361-1368
Series
2018 21st International Conference on Information Fusion (FUSION)
Abstract
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.
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Citation
Solin, A, Cortes Reina, S, Rahtu, E & Kannala, J 2018, Inertial Odometry on Handheld Smartphones . in 2018 21st International Conference on Information Fusion, FUSION 2018 ., 8455482, IEEE, pp. 1361-1368, International Conference on Information Fusion, Cambridge, United Kingdom, 10/07/2018 . https://doi.org/10.23919/ICIF.2018.8455482