Inertial sensors are widely utilized in smartphones, drones, robots, and IoT devices, playing a crucial role in enabling ubiquitous and reliable localization. Inertial sensor-based positioning is essential in various applications, including personal navigation, location-based security, and human-device interaction. However, low-cost MEMS inertial sensors' measurements are inevitably corrupted by various error sources, leading to unbounded drifts when integrated doubly in traditional inertial navigation algorithms, subjecting inertial positioning to the problem of error drifts. In recent years, with the rapid increase in sensor data and computational power, deep learning techniques have been developed, sparking significant research into addressing the problem of inertial positioning. Relevant literature in this field spans across mobile computing, robotics, and machine learning. In this article, we provide a comprehensive review of deep learning-based inertial positioning and its applications in tracking pedestrians, drones, vehicles, and robots. We connect efforts from different fields and discuss how deep learning can be applied to address issues such as sensor calibration, positioning error drift reduction, and multi-sensor fusion. This article aims to attract readers from various backgrounds, including researchers and practitioners interested in the potential of deep learning-based techniques to solve inertial positioning problems. Our review demonstrates the exciting possibilities that deep learning brings to the table and provides a roadmap for future research in this field.

翻译：惯性传感器广泛应用于智能手机、无人机、机器人及物联网设备中，是实现普适可靠定位的关键技术。基于惯性传感器的定位在个人导航、位置安全认证及人机交互等领域具有重要价值。然而，低成本MEMS惯性传感器的测量值不可避免地受到多种误差源干扰，导致传统惯性导航算法中双重积分过程出现无界漂移，使得惯性定位面临误差漂移问题。近年来，随着传感器数据量和计算能力的快速增长，深度学习技术得到发展，催生了大量针对惯性定位问题的研究。该领域相关文献横跨移动计算、机器人学和机器学习等多个学科。本文全面综述了基于深度学习的惯性定位技术及其在行人、无人机、车辆和机器人跟踪中的应用，整合了不同领域的研究成果，探讨了如何利用深度学习解决传感器校准、定位误差漂移抑制及多传感器融合等问题。本文旨在吸引不同背景的读者，包括对利用深度学习技术解决惯性定位问题感兴趣的科研人员与工程实践者。本综述展示了深度学习在该领域展现的激动人心的可能性，并为未来研究提供了路线图。