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.

翻译：惯性传感广泛应用于从智能手机等日常设备到自动驾驶汽车等复杂系统的各类平台中。近年来，机器学习和深度学习技术在惯性传感与传感器融合领域取得了显著发展，这得益于高效计算硬件的进步以及公开传感器数据的可获取性。这些数据驱动方法主要旨在增强基于模型的惯性传感算法。为促进深度学习与惯性导航及融合技术的交叉研究并充分发挥其潜力，本文对面向惯性传感与传感器融合的深度学习方法进行了深度综述。我们探讨了用于标定与去噪的学习方法，以及改进纯惯性导航与传感器融合的技术途径——后者主要通过学习融合滤波器的部分参数实现。根据车辆运行环境（陆地、空中、海洋），对综述的方法进行了分类。此外，我们还分析了基于深度学习的导航技术发展趋势与未来方向，并提供了常用方法的统计数据。