Inertial sensor has been widely deployed on smartphones, drones, robots and IoT devices. Due to its importance in ubiquitous and robust localization, inertial sensor based positioning is key in many applications, including personal navigation, location based security, and human-device interaction. However, inertial positioning suffers from the so-called error drifts problem, as the measurements of low-cost MEMS inertial sensor are corrupted with various inevitable error sources, leading to unbounded drifts when being integrated doubly in traditional inertial navigation algorithms. Recently, with increasing sensor data and computational power, the fast developments in deep learning have spurred a large amount of research works in introducing deep learning to tackle the problem of inertial positioning. Relevant literature spans from the areas of mobile computing, robotics and machine learning. This article comprehensively reviews relevant works on deep learning based inertial positioning, connects the efforts from different fields, and covers how deep learning can be applied to solve sensor calibration, positioning error drifts reduction and sensor fusion. Finally, we provide insights on the benefits and limitations of existing works, and indicate the future opportunities in this direction.

翻译：惯性传感器已广泛部署于智能手机、无人机、机器人和物联网设备。由于其在普适性与鲁棒定位中的重要性，基于惯性传感器的定位已成为众多应用的关键技术，包括个人导航、基于位置的安全以及人机交互。然而，惯性定位面临所谓的误差漂移问题，这是因为低成本MEMS惯性传感器的测量值受到多种不可避免的误差源污染，在传统惯性导航算法中经历二次积分后会导致无界漂移。近年来，随着传感器数据量和计算能力的提升，深度学习的快速发展催生了大量引入深度学习解决惯性定位问题的研究工作。相关文献涵盖了移动计算、机器人和机器学习等领域。本文全面回顾了基于深度学习的惯性定位相关研究，连接了不同领域的工作，并涵盖了如何应用深度学习解决传感器校准、定位误差漂移减少以及传感器融合等问题。最后，我们提供了对现有工作的优势与局限性的见解，并指出了该方向的未来机遇。