HeadSLAM: Pedestrian SLAM with Head-Mounted Sensors.
HeadSLAM: Pedestrian SLAM with Head-Mounted Sensors.
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DOI：
10.3390/s22041593
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发表时间：
2022-02-18
期刊：
影响因子：
--
通讯作者：
Bergmann J
中科院分区：
文献类型：
--
作者：
Hou X;Bergmann J
近年来，基于可穿戴传感器的人体位置跟踪研究发展迅速，在医疗保健、智能家居、体育和紧急服务等领域显示出巨大的应用潜力。带有惯性测量单元（伊穆斯）的行人航位推算（PDR）是该领域中最有前途的解决方案之一，因为它不依赖于任何额外的基础设施，同时也适用于各种场景。然而，PDR仅在由于漂移导致的无界误差影响位置估计之前的有限时间段内是准确的。误差校正可能很困难，因为通常缺乏有效的校准方法。HeadSLAM是一种专门为头戴式伊穆斯设计的方法，它可以在较长的跟踪时间（10 min）内提高精度。研究参与者（n = 7）被要求佩戴头戴式传感器在室内和室外环境中行走，随后将获得的HeadSLAM精度与PDR方法进行比较。两种方法的平均均方根误差和绝对误差有显著性差异（p < 0.001）。在20小时步行数据集中，HeadSLAM在所有场景和受试者中具有较低的误差。这项研究的结果显示了HeadSLAM算法如何为头戴式低成本传感器提供更准确的长期定位服务。改进的性能可以支持廉价的无基础设施导航应用。
Research focused on human position tracking with wearable sensors has been developing rapidly in recent years, and it has shown great potential for application within healthcare, smart homes, sports, and emergency services. Pedestrian Dead Reckoning (PDR) with Inertial Measurement Units (IMUs) is one of the most promising solutions within this domain, as it does not rely on any additional infrastructure, whilst also being suitable for use in a diverse set of scenarios. However, PDR is only accurate for a limited period of time before unbounded errors, due to drift, affect the position estimate. Error correction can be difficult as there is often a lack of efficient methods for calibration. HeadSLAM, a method specifically designed for head-mounted IMUs, is proposed to improve the accuracy during longer tracking times (10 min). Research participants (n = 7) were asked to walk in both indoor and outdoor environments wearing head-mounted sensors, and the obtained HeadSLAM accuracy was subsequently compared to that of the PDR method. A significant difference (p < 0.001) in the average root-mean-squared error and absolute error was found between the two methods. HeadSLAM had a consist lower error across all scenarios and subjects in a 20 h walking dataset. The findings of this study show how the HeadSLAM algorithm can provide a more accurate long-term location service for head-mounted, low-cost sensors. The improved performance can support inexpensive applications for infrastructureless navigation.
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