  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.
