Abstract

Pedestrian navigation has been of high interest in many fields, such as human health monitoring, personal indoor navigation, and localization systems for first responders. Due to the potentially complicated navigation environment, selfcontained types of navigation such as inertial navigation, which do not depend on external signals, are more desirable. Pure inertial navigation, however, suffers from sensor noise and drifts and therefore is not suitable for long-term pedestrian navigation by itself. Zero-velocity update (ZUPT) aiding technique has been developed to limit the navigation error growth, but adaptivity of algorithms, model fidelity, and system robustness have been major a concern if not properly addressed. In this paper, we attempt to establish a common approach to solve the problem of self-contained pedestrian navigation by identifying the critical parts of the algorithm that have a strong influence on the overall performance. We first review approaches to improve the navigation accuracy in each of the critical part of implementation proposed by other groups. Then, we report our results on analytical estimations and experiments illustrating effects of combining inertial sensor calibration, stance phase detection, adaptive model selection, and sensor fusion.

中文翻译：

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场景相关的 ZUPT 辅助行人惯性导航与传感器融合

摘要

行人导航在许多领域都受到高度关注，例如人体健康监测、个人室内导航和急救人员的定位系统。由于潜在的复杂导航环境，更需要独立类型的导航，例如不依赖外部信号的惯性导航。然而，纯惯性导航受到传感器噪声和漂移的影响，因此本身不适合长期行人导航。已经开发了零速度更新 (ZUPT) 辅助技术来限制导航误差的增长，但如果没有得到妥善解决，算法的适应性、模型保真度和系统鲁棒性一直是一个主要问题。在本文中，我们试图通过识别算法中对整体性能有很大影响的关键部分来建立一种通用方法来解决独立行人导航的问题。我们首先回顾了其他小组提出的在每个关键实施部分中提高导航准确性的方法。然后，我们报告分析估计和实验的结果，说明结合惯性传感器校准、姿态相位检测、自适应模型选择和传感器融合的效果。