Deep learning based speed estimation for constraining strapdown inertial navigation on smartphones

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Abstract

Strapdown inertial navigation systems are sensitive to the quality of the data provided by the accelerometer and gyroscope. Low-grade IMUs in handheld smart-devices pose a problem for inertial odometry on these devices. We propose a scheme for constraining the inertial odometry problem by complementing non-linear state estimation by a CNN-based deep-learning model for inferring the momentary speed based on a window of IMU samples. We show the feasibility of the model using a wide range of data from an iPhone, and present proof-of-concept results for how the model can be combined with an inertial navigation system for three-dimensional inertial navigation.
Original languageEnglish
Title of host publication2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)
Place of PublicationAalborg
PublisherIEEE
Pages1-6
ISBN (Electronic)978-1-5386-5477-4
DOIs
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventIEEE International Workshop on Machine Learning for Signal Processing - Aalborg, Denmark
Duration: 17 Sep 201820 Sep 2018
Conference number: 28

Workshop

WorkshopIEEE International Workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP
CountryDenmark
CityAalborg
Period17/09/201820/09/2018

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  • Cite this

    Cortes Reina, S., Solin, A., & Kannala, J. (2018). Deep learning based speed estimation for constraining strapdown inertial navigation on smartphones. In 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1-6). [8516710] Aalborg: IEEE. https://doi.org/10.1109/MLSP.2018.8516710