FootSLAM Meets Adaptive Thresholding
FootSLAM Meets Adaptive Thresholding
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DOI：
10.1109/jsen.2020.2987813
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发表时间：
2020-08-15
影响因子：
4.3
通讯作者：
Trigoni, Niki
中科院分区：
文献类型：
--
作者：
Wahlstrom, Johan;Markham, Andrew;Trigoni, Niki
零速度检测阈值的标定是实现零速度辅助惯性导航的必要前提。然而，文献缺乏一种独立的校准方法，适合在没有地图信息或预部署基础设施的未准备环境中大规模使用。本文将零速度检测阈值的标定表述为极大似然问题。使用从FootSLAM算法中容易获得的估计量来近似似然函数。因此，我们获得了一种不需要地图信息、补充传感器测量或用户输入的自适应阈值方法。实验评估使用不同的步态速度、传感器位置和行走轨迹的数据进行。所提出的校准方法优于固定阈值零速度检测器和使用基于速度的阈值分类器的基准。
Calibration of the zero-velocity detection threshold is an essential prerequisite for zero-velocity-aided inertial navigation. However, the literature is lacking a self-contained calibration method, suitable for large-scale use in unprepared environments without map information or pre-deployed infrastructure. In this paper, the calibration of the zero-velocity detection threshold is formulated as a maximum likelihood problem. The likelihood function is approximated using estimation quantities readily available from the FootSLAM algorithm. Thus, we obtain a method for adaptive thresholding that does not require map information, measurements from supplementary sensors, or user input. Experimental evaluations are conducted using data with different gait speeds, sensor placements, and walking trajectories. The proposed calibration method is shown to outperform fixed-threshold zero-velocity detectors and a benchmark using a speed-based threshold classifier.