  Inertial Measurement Units (IMUs) are interceptive modalities that provide
ego-motion measurements independent of the environmental factors. They are
widely adopted in various autonomous systems. Motivated by the limitations in
processing the noisy measurements from these sensors using their mathematical
models, researchers have recently proposed various deep learning architectures
to estimate inertial odometry in an end-to-end manner. Nevertheless, the
high-frequency and redundant measurements from IMUs lead to long raw sequences
to be processed. In this study, we aim to investigate the efficacy of accurate
preintegration as a more realistic solution to the IMU motion model for deep
inertial odometry (DIO) and the resultant DIO is a fusion of model-driven and
data-driven approaches. The accurate IMU preintegration has the potential to
outperform numerical approximation of the continuous IMU model used in the
existing DIOs. Experimental results validate the proposed DIO.
