Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones

08/10/2018
by   Santiago Cortes, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset