Abstract

The traditional Zero Velocity Updating algorithm can be used to correct the accumulated errors of the device effectively. However, as the threshold value of the traditional Zero Velocity Updating algorithm is fixed, it is only suitable for a single motion mode. When indoor pedestrian motion includes multiple motion modes, the positioning accuracy will be greatly reduced. In this paper, we propose a half-voting random forest algorithm, an adaptive Zero Velocity Updating method for multi-motion mode using half-voting Random Forest. Half-voting Random Forest Algorithm means that in the decision tree voting process, the decision tree stops voting when the voting number reaches half of the voting volume. We determined the optimal thresholds of Zero Velocity Updating value for standing still, walking, running, going upstairs and downstairs for the indoor pedestrian. Then we recognize pedestrian motion modes by Random Forest with half-voting and weighted decision trees. Finally, we assign the optimal threshold based on the motion modes for Zero Velocity Updating. In order to verify the feasibility and effectiveness of the method proposed in this paper, field experiments were carried out with the inertial navigation module developed by our laboratory. The experimental results show that when indoor pedestrians perform multi-mode motion, the positioning error is 0.5 m.

中文翻译：

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半投票随机森林算法及其在室内行人导航中的应用

摘要

传统的零速度更新算法可以有效地校正设备的累积误差。但是，由于传统的零速度更新算法的阈值是固定的，因此仅适用于单运动模式。当室内行人运动包括多种运动模式时，定位精度将大大降低。在本文中，我们提出了一种半投票随机森林算法，一种使用半投票随机森林的多运动模式自适应零速度更新方法。半投票随机森林算法意味着在决策树投票过程中，当投票数达到一半投票量时，决策树将停止投票。我们确定了静止，步行，跑步，为室内行人上下楼。然后，我们通过具有半投票和加权决策树的随机森林识别行人运动模式。最后，我们基于零速度更新的运动模式分配最佳阈值。为了验证本文提出的方法的可行性和有效性，利用我所实验室开发的惯性导航模块进行了现场实验。实验结果表明，室内行人多模式运动时，定位误差为0.5 m。为了验证本文提出的方法的可行性和有效性，利用我所实验室开发的惯性导航模块进行了现场实验。实验结果表明，室内行人多模式运动时，定位误差为0.5 m。为了验证本文提出的方法的可行性和有效性，利用我所实验室开发的惯性导航模块进行了现场实验。实验结果表明，室内行人多模式运动时，定位误差为0.5 m。