Zero velocity update is a common and efficient approach to bound the accumulated error growth for foot-mounted inertial navigation system. Thus a robust zero velocity detector (ZVD) for all kinds of locomotion is needed for high accuracy pedestrian navigation systems. In this paper, we investigate two machine learning-based ZVDs: Histogram-based Gradient Boosting (HGB) and Random Forest (RF), aiming at adapting to different motion types while reducing the computation costs compared to the deep learning-based detectors. A complete data pre-processing procedure, including a feature engineering study and data augmentation techniques, is proposed. A motion classifier based on HGB is used to distinguish “single support” and “double float” motions. This concept is different from the traditional locomotion classification (walking, running, stair climbing) since it merges similar motions into the same class. The proposed ZVDs are evaluated with inertial data collected by two subjects over a 1.8 km indoor/outdoor path with different motions and speeds. The results show that without huge training dataset, these two machine learning-based ZVDs achieve better performances (55 cm positioning accuracy) and lower computational costs than the two deep learning-based Long Short-Term Memory methods (1.21 m positioning accuracy).

中文翻译：

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基于机器学习的惯性行人导航零速度检测

零速度更新是一种常见且有效的方法来限制脚踏式惯导系统的累积误差增长。因此，高精度行人导航系统需要适用于各种运动的稳健零速度检测器 (ZVD)。在本文中，我们研究了两种基于机器学习的 ZVD：基于直方图的梯度提升 (HGB) 和随机森林 (RF)，旨在适应不同的运动类型，同时与基于深度学习的检测器相比降低计算成本。提出了一个完整的数据预处理程序，包括特征工程研究和数据增强技术。基于 HGB 的运动分类器用于区分“单支撑”和“双浮动”运动。这个概念不同于传统的运动分类（步行、跑步、爬楼梯），因为它将类似的动作合并到同一类中。所提出的 ZVD 是用两个对象在 1.8 公里的室内/室外路径上以不同的运动和速度收集的惯性数据进行评估的。结果表明，在没有庞大的训练数据集的情况下，这两种基于机器学习的 ZVD 实现了比两种基于深度学习的 Long Short-Term Memory 方法（1.21 m 定位精度）更好的性能（55 cm 定位精度）和更低的计算成本。