Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones
This addresses inertial navigation accuracy issues for smartphone users, but it is incremental as it builds on existing methods with a new constraint.
The paper tackled the problem of low-grade IMUs in smartphones degrading inertial odometry by proposing a CNN-based deep learning model to estimate momentary speed from IMU data, showing feasibility with iPhone data and proof-of-concept integration for 3D navigation.
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.