赵林生, 王鸿鹏, 刘景泰. 人机共享环境下基于Wi-Fi指纹的室内定位方法[J]. 机器人, 2019, 41(3): 404-413. DOI: 10.13973/j.cnki.robot.180307
引用本文: 赵林生, 王鸿鹏, 刘景泰. 人机共享环境下基于Wi-Fi指纹的室内定位方法[J]. 机器人, 2019, 41(3): 404-413. DOI: 10.13973/j.cnki.robot.180307
ZHAO Linsheng, WANG Hongpeng, LIU Jingtai. An Indoor Localization Method Based on Wi-Fi Fingerprint in the Human-Robot Shared Environment[J]. ROBOT, 2019, 41(3): 404-413. DOI: 10.13973/j.cnki.robot.180307
Citation: ZHAO Linsheng, WANG Hongpeng, LIU Jingtai. An Indoor Localization Method Based on Wi-Fi Fingerprint in the Human-Robot Shared Environment[J]. ROBOT, 2019, 41(3): 404-413. DOI: 10.13973/j.cnki.robot.180307

人机共享环境下基于Wi-Fi指纹的室内定位方法

An Indoor Localization Method Based on Wi-Fi Fingerprint in the Human-Robot Shared Environment

  • 摘要: 针对服务机器人和行人的室内全局定位问题,提出一种人机共享环境下基于Wi-Fi指纹的室内定位方法.首先,采用核主成分分析法(KPCA)从双频段的Wi-Fi信号中提取一种设备无关的鲁棒位置指纹,用于Wi-Fi指纹定位.然后,为了提高行人定位的稳定性和精确度,结合行人航迹推算(PDR)的定位方法,设计了一种基于选择更新粒子滤波(SUPF)的Wi-Fi/PDR组合定位算法.在该算法中,利用PDR对移动场景下的Wi-Fi定位结果进行了初步校正,并通过定义自适应大小的可信空间对校正后的结果进行评估,从而在数据融合之前剔除不可信的Wi-Fi定位估计.最后,在实际场景下开展了定位实验,Wi-Fi/PDR组合定位的平均定位误差约为2 m,实验结果表明所提出的方法提升了定位系统的精确度和鲁棒性.

     

    Abstract: An indoor localization method is proposed based on Wi-Fi fingerprint in the human-robot shared environment, in order to solve the problem of indoor global localization for service robots and pedestrians. Firstly, the device-independent robust position fingerprint is extracted from dual-band Wi-Fi signals by kernel principal component analysis (KPCA) for WiFi fingerprinting. Then, combining with the pedestrian dead reckoning (PDR) method, a Wi-Fi/PDR integrated positioning algorithm is presented based on selective update particle filter (SUPF) to improve the stability and the accuracy of pedestrian positioning. In the algorithm, the Wi-Fi localization results in the moving scene are preliminarily corrected using PDR, and the corrected results are evaluated by defining the trusted space of an adaptive size, so that untrusted Wi-Fi localization estimations are removed before the data fusion. Finally, localization experiments are carried out in a real scenario, and the average positioning error of the Wi-Fi/PDR integrated positioning algorithm is about 2 m. Experimental results demonstrate that the proposed method improves the accuracy and the robustness of the positioning system.

     

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