Inertial Odometry on Handheld Smartphones

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Solin, Arno
dc.contributor.author Cortes Reina, Santiago
dc.contributor.author Rahtu, Esa
dc.contributor.author Kannala, Juho
dc.date.accessioned 2018-12-10T10:27:04Z
dc.date.available 2018-12-10T10:27:04Z
dc.date.issued 2018
dc.identifier.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) . Institute of Electrical and Electronics Engineers , pp. 1361-1368 , International Conference on Information Fusion , Cambridge , United Kingdom , 10/07/2018 . DOI: 10.23919/ICIF.2018.8455482 en
dc.identifier.isbn 978-0-9964527-7-9
dc.identifier.other PURE UUID: b097c8de-28be-4d2e-995a-0d83a171251c
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/inertial-odometry-on-handheld-smartphones(b097c8de-28be-4d2e-995a-0d83a171251c).html
dc.identifier.other PURE LINK: https://arxiv.org/abs/1703.00154
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/30201906/1703.00154.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/35214
dc.description.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. en
dc.format.extent 1361-1368
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartof International Conference on Information Fusion en
dc.relation.ispartofseries 2018 21st International Conference on Information Fusion (FUSION) en
dc.rights openAccess en
dc.subject.other 113 Computer and information sciences en
dc.title Inertial Odometry on Handheld Smartphones en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science
dc.contributor.department Tampere University of Technology
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201812106229
dc.identifier.doi 10.23919/ICIF.2018.8455482
dc.type.version acceptedVersion


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