  Inertial sensing is used in many applications and platforms, ranging from
day-to-day devices such as smartphones to very complex ones such as autonomous
vehicles. In recent years, the development of machine learning and deep
learning techniques has increased significantly in the field of inertial
sensing and sensor fusion. This is due to the development of efficient
computing hardware and the accessibility of publicly available sensor data.
These data-driven approaches mainly aim to empower model-based inertial sensing
algorithms. To encourage further research in integrating deep learning with
inertial navigation and fusion and to leverage their capabilities, this paper
provides an in-depth review of deep learning methods for inertial sensing and
sensor fusion. We discuss learning methods for calibration and denoising as
well as approaches for improving pure inertial navigation and sensor fusion.
The latter is done by learning some of the fusion filter parameters. The
reviewed approaches are classified by the environment in which the vehicles
operate: land, air, and sea. In addition, we analyze trends and future
directions in deep learning-based navigation and provide statistical data on
commonly used approaches.
