深度学习辅助的惯性航位推算与融合
Deep Learning Assisted Inertial Dead Reckoning and Fusion
AI总结：
针对GNSS中断下惯性导航漂移问题，提出深度学习增强的周期性运动惯性距离回归与神经-惯性融合滤波方法，并在机器人和四旋翼337分钟数据上验证。
摘要：
近年来，移动平台在各类应用中的关注度显著提升。原因之一在于能够利用低成本传感器实现精确导航。为此，惯性传感器会与全球导航卫星系统（GNSS）信号进行融合。平台运行期间的GNSS中断可能导致纯惯性导航，从而使导航解算发生漂移。在这种情况下，已有研究提出采用周期性轨迹和专用算法来缓解漂移。在周期性动力学条件下，惯性深度学习方法能够更准确地捕捉运动，并为无人机和移动机器人提供精确的航位推算。本文提出若干方法，以扩展周期性运动期间深度学习辅助的惯性感知与融合能力。我们首先证明，与直线轨迹相比，GNSS与惯性传感器在周期性轨迹中的融合可获得更高精度。接着，我们提出一种增强型网络架构，用于精确回归平台的距离变化。利用该网络，我们推动了一种用于神经-惯性融合滤波器的混合方法。最后，我们将该方法用于GNSS可用的情形，并展示其优势。研究使用一个包含337分钟数据的数据集评估所提方法，这些数据采集自安装在移动机器人和四旋翼飞行器上的惯性传感器。
英文摘要：
The interest in mobile platforms across a variety of applications has increased significantly in recent years. One of the reasons is the ability to achieve accurate navigation by using low-cost sensors. To this end, inertial sensors are fused with global navigation satellite systems (GNSS) signals. GNSS outages during platform operation can result in pure inertial navigation, causing the navigation solution to drift. In such situations, periodic trajectories with dedicated algorithms were suggested to mitigate the drift. With periodic dynamics, inertial deep learning approaches can capture the motion more accurately and provide accurate dead-reckoning for drones and mobile robots. In this paper, we propose approaches to extend deep learning-assisted inertial sensing and fusion capabilities during periodic motion. We begin by demonstrating that fusion between GNSS and inertial sensors in periodic trajectories achieves better accuracy compared to straight-line trajectories. Next, we propose an empowered network architecture to accurately regress the change in distance of the platform. Utilizing this network, we drive a hybrid approach for a neural-inertial fusion filter. Finally, we utilize this approach for situations when GNSS is available and show its benefits. A dataset of 337 minutes of data collected from inertial sensors mounted on a mobile robot and a quadrotor is used to evaluate our approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
