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Carrying Position Independent User Heading Estimation for Indoor Pedestrian Navigation with Smartphones

机译:室内行人导航与智能手机的携带位置独立用户航向估计

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摘要

This paper proposes a novel heading estimation approach for indoor pedestrian navigation using the built-in inertial sensors on a smartphone. Unlike previous approaches constraining the carrying position of a smartphone on the user’s body, our approach gives the user a larger freedom by implementing automatic recognition of the device carrying position and subsequent selection of an optimal strategy for heading estimation. We firstly predetermine the motion state by a decision tree using an accelerometer and a barometer. Then, to enable accurate and computational lightweight carrying position recognition, we combine a position classifier with a novel position transition detection algorithm, which may also be used to avoid the confusion between position transition and user turn during pedestrian walking. For a device placed in the trouser pockets or held in a swinging hand, the heading estimation is achieved by deploying a principal component analysis (PCA)-based approach. For a device held in the hand or against the ear during a phone call, user heading is directly estimated by adding the yaw angle of the device to the related heading offset. Experimental results show that our approach can automatically detect carrying positions with high accuracy, and outperforms previous heading estimation approaches in terms of accuracy and applicability.
机译:本文提出了一种使用智能手机上的内置惯性传感器进行室内行人导航的新颖航向估计方法。与以前的方法限制智能手机在用户身上的携带位置不同,我们的方法通过实现设备携带位置的自动识别以及随后选择最佳航向估计策略,为用户提供了更大的自由度。我们首先使用加速度计和气压计通过决策树预先确定运动状态。然后,为了实现精确的计算轻量级携带位置识别,我们将位置分类器与新颖的位置转换检测算法相结合,该算法还可用于避免行人行走过程中位置转换与用户转弯之间的混淆。对于放置在裤子口袋中或握在手中的设备,航向估计是通过部署基于主成分分析(PCA)的方法来实现的。对于在通话过程中握在手中或靠在耳朵上的设备,可以通过将设备的偏航角与相关的航向偏移量相加来直接估算用户的航向。实验结果表明,我们的方法可以自动高精度地检测出运载位置,并且在准确性和适用性方面都优于以前的航向估计方法。

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