Tampere University of Technology

TUTCRIS Research Portal

Inertial Odometry on Handheld Smartphones

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Standard

Inertial Odometry on Handheld Smartphones. / Solin, Arno; Cortes, Santiago; Rahtu, Esa; Kannala, Juho.

2018 21st International Conference on Information Fusion, FUSION 2018. IEEE, 2018. p. 1361-1368 8455482.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Solin, A, Cortes, 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, 10/07/18. https://doi.org/10.23919/ICIF.2018.8455482

APA

Solin, A., Cortes, S., Rahtu, E., & Kannala, J. (2018). Inertial Odometry on Handheld Smartphones. In 2018 21st International Conference on Information Fusion, FUSION 2018 (pp. 1361-1368). [8455482] IEEE. https://doi.org/10.23919/ICIF.2018.8455482

Vancouver

Solin A, Cortes S, Rahtu E, Kannala J. Inertial Odometry on Handheld Smartphones. In 2018 21st International Conference on Information Fusion, FUSION 2018. IEEE. 2018. p. 1361-1368. 8455482 https://doi.org/10.23919/ICIF.2018.8455482

Author

Solin, Arno ; Cortes, Santiago ; Rahtu, Esa ; Kannala, Juho. / Inertial Odometry on Handheld Smartphones. 2018 21st International Conference on Information Fusion, FUSION 2018. IEEE, 2018. pp. 1361-1368

Bibtex - Download

@inproceedings{e5f7a58663a448c18c16efe6ed5e33af,
title = "Inertial Odometry on Handheld Smartphones",
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.",
author = "Arno Solin and Santiago Cortes and Esa Rahtu and Juho Kannala",
year = "2018",
month = "9",
day = "5",
doi = "10.23919/ICIF.2018.8455482",
language = "English",
isbn = "9780996452762",
pages = "1361--1368",
booktitle = "2018 21st International Conference on Information Fusion, FUSION 2018",
publisher = "IEEE",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Inertial Odometry on Handheld Smartphones

AU - Solin, Arno

AU - Cortes, Santiago

AU - Rahtu, Esa

AU - Kannala, Juho

PY - 2018/9/5

Y1 - 2018/9/5

N2 - 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.

AB - 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.

U2 - 10.23919/ICIF.2018.8455482

DO - 10.23919/ICIF.2018.8455482

M3 - Conference contribution

SN - 9780996452762

SP - 1361

EP - 1368

BT - 2018 21st International Conference on Information Fusion, FUSION 2018

PB - IEEE

ER -