Indoor localization system based on wearable posture sensors with incomplete observations

Yuan Wang, Jian Huang, Yongji Wang, Chunjing Tao, Heping Yan, Lifang Ma
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引用次数: 5

Abstract

Radio Frequency Identification (RFID) based indoor localization becomes a hotspot in the robotic research field recently. To overcome the shortcoming that plentiful tags are required in a normal RFID based localization system, this paper presents an indoor localization method by fusing measurements from wearable posture sensors and the absolute position information from scattered RFID tags. From the posture sensors, we can obtain the relative indoor localization data by summing up the vectors composed of step length and heading direction. Since this relative localization is highly affected by the cumulative error, the absolute positions of RFID tags are used as corrections if they are found within a read-range to the user. Because the RFID tags are sparsely placed in the indoor environment, the corrections can be achieved only at incomplete time instants. Therefore, a revised Kalman filter with incomplete observation is applied to the sensor fusion between the posture sensors and RFID tags. Experimental results show that the cumulative error of the system can be significantly reduced and the localization accuracy is enhanced through the sensor fusion.
基于不完全观测可穿戴姿态传感器的室内定位系统
基于射频识别(RFID)的室内定位技术是近年来机器人领域的研究热点。针对传统RFID定位系统需要大量标签的缺点,提出了一种融合可穿戴式姿态传感器测量数据和分散RFID标签绝对位置信息的室内定位方法。从姿态传感器中,我们可以通过将步长和方向矢量相加得到相对的室内定位数据。由于这种相对定位受累积误差的影响很大,因此如果在用户的可读范围内发现RFID标签的绝对位置,则将其用作更正。由于RFID标签被稀疏地放置在室内环境中,因此只能在不完整的时间瞬间进行校正。因此,将一种改进的不完全观测卡尔曼滤波器应用于姿态传感器与RFID标签之间的传感器融合。实验结果表明,通过传感器融合可以显著降低系统的累积误差,提高定位精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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