An Indoor Localization Method Based on Wi-Fi Fingerprint in the Human-Robot Shared Environment
ZHAO Linsheng1,2, WANG Hongpeng1,2, LIU Jingtai1,2
1. Institute of Robotics and Automatic Information System, Nankai University, Tianjin 300350, China;
2. Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300353, China
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. ROBOT, 2019, 41(3): 404-413. DOI: 10.13973/j.cnki.robot.180307.
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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