  MEMS Inertial Measurement Units (IMUs) as ubiquitous proprioceptive motion
measurement devices are available on various everyday gadgets and robotic
platforms. Nevertheless, the direct inference of geometrical transformations or
odometry based on these data alone is a challenging task. This is due to the
hard-to-model imperfections and high noise characteristics of the sensor, which
has motivated research in formulating the system as an end-to-end learning
problem, where the motion patterns of the agent are exploited to facilitate
better odometry estimates. However, this benefit comes at the cost of high
computation and memory requirements, which makes deep inertial odometry
unsuitable for low-power and edge applications. This paper attempts to address
this conflict by proposing the IMU preintegrated features as a replacement for
the raw IMU data in deep inertial odometry. Exploiting the manifold structure
of the IMU motion model, these features provide a temporally compressed motion
representation that preserves important geometrical information. We demonstrate
the effectiveness and efficiency of this approach for the task of inertial
odometry on two applications of pedestrian motion estimation and autonomous
vehicles. We show a performance improvement compared to raw inputs while
reducing the computational burdens. Additionally, we demonstrate the efficiency
of this approach through an embedded implementation on a resource-constrained
microcontroller.
