Deep learning based speed estimation for constraining strapdown inertial navigation on smartphones
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A4 Artikkeli konferenssijulkaisussa
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en
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2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1-6
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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.Description
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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)., 8516710, IEEE, Aalborg, pp. 1-6, IEEE International Workshop on Machine Learning for Signal Processing, Aalborg, Denmark, 17/09/2018. https://doi.org/10.1109/MLSP.2018.8516710