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.
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 Wi-Fi 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.
作者
赵林生
王鸿鹏
刘景泰
ZHAO Linsheng;WANG Hongpeng;LIU Jingtai(Institute of Robotics and Automatic Information System,Nankai University,Tianjin 300350,China;Tianjin Key Laboratory of Intelligent Robotics,Nankai University,Tianjin 300353,China)
出处
《机器人》
EI
CSCD
北大核心
2019年第3期404-413,共10页
Robot
基金
国家自然科学基金(61375087,61573198)
天津市应用基础与前沿技术研究计划(15JCZDJC31200,14ZCD2GX00798)
关键词
服务机器人
Wi-Fi指纹定位
共融机器人
行人航迹推算
粒子滤波
service robot
Wi-Fi fingerprint localization
tri-co(coexisting-cooperative-cognitive) robot
pedestrian dead reckoning(PDR)
particle filter
