Inertial Odometry on Handheld Smartphones

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

Details

Original languageEnglish
Title of host publication2018 21st International Conference on Information Fusion, FUSION 2018
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Information Fusion - Cambridge, United Kingdom
Duration: 10 Jul 201813 Jul 2018
Conference number: 21

Conference

ConferenceInternational Conference on Information Fusion
Abbreviated titleFUSION
CountryUnited Kingdom
CityCambridge
Period10/07/201813/07/2018

Researchers

Research units

  • Tampere University of Technology

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.

Download statistics

No data available

ID: 30151750