Abstract
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.
Abstract (translated)
惯性传感器已经广泛应用于智能手机、无人机、机器人和物联网设备中。由于其在无处不在和鲁棒定位方面的重要角色,惯性传感器在许多应用中是至关重要的,包括个人导航、位置安全性和人机互动。然而,惯性定位存在所谓的误差漂移问题,因为低成本微机电惯性传感器的测量值受到各种不可避免的误差源的影响,导致在传统惯性导航算法中叠加两次后出现无限级的漂移。最近,随着传感器数据和计算能力的不断增加,深度学习的迅速发展促使了大量研究 work 引入深度学习来解决惯性定位问题。相关文献涵盖了移动计算、机器人学和机器学习等领域。本文全面综述了基于深度学习的惯性定位相关的研究 work,并连接了来自不同领域的努力,并探讨了深度学习如何用于解决传感器校准、定位误差漂移s减少和传感器融合等问题。最后,我们提供了现有工作的有益和限制 insights,并展望了这一领域的未来机会。
URL
https://arxiv.org/abs/2303.03757
