Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions
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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. This is due to the development of efficient computing hardware and the accessibility of publicly available sensor data. These data-driven approaches are used to empower model-based navigation and sensor fusion algorithms. This paper provides an in-depth review of those deep learning methods. We examine separately, each vehicle operation domain including land, air, and sea. Each domain is divided into pure inertial advances and improvements based on filter parameters learning. In addition, we review deep learning approaches for calibrating and denoising inertial sensors. Throughout the paper, we discuss these trends and future directions. We also provide statistics on the commonly used approaches to illustrate their efficiency and stimulate further research in deep learning embedded in inertial navigation and fusion.
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