Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones
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.
Cite
@article{arxiv.1808.03485,
title = {Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones},
author = {Santiago Cortés and Arno Solin and Juho Kannala},
journal= {arXiv preprint arXiv:1808.03485},
year = {2018}
}
Comments
To appear in IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2018