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结合 VSLAM 和磁指纹图以提高室内定位精度。

Combination of VSLAM and a Magnetic Fingerprint Map to Improve Accuracy of Indoor Positioning.

机构信息

Department of Land Economics, National Chengchi University, Taipei 11605, Taiwan.

GIS Research Center, Feng Chia University, Taichung 40724, Taiwan.

出版信息

Sensors (Basel). 2022 Nov 28;22(23):9244. doi: 10.3390/s22239244.

DOI:10.3390/s22239244
PMID:36501945
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9738752/
Abstract

With the continual advancement of positioning technology, people's use of mobile devices has increased substantially. The global navigation satellite system (GNSS) has improved outdoor positioning performance. However, it cannot effectively locate indoor users owing to signal masking effects. Common indoor positioning technologies include radio frequencies, image visions, and pedestrian dead reckoning. However, the advantages and disadvantages of each technology prevent a single indoor positioning technology from solving problems related to various environmental factors. In this study, a hybrid method was proposed to improve the accuracy of indoor positioning by combining visual simultaneous localization and mapping (VSLAM) with a magnetic fingerprint map. A smartphone was used as an experimental device, and a built-in camera and magnetic sensor were used to collect data on the characteristics of the indoor environment and to determine the effect of the magnetic field on the building structure. First, through the use of a preestablished indoor magnetic fingerprint map, the initial position was obtained using the weighted k-nearest neighbor matching method. Subsequently, combined with the VSLAM, the Oriented FAST and Rotated BRIEF (ORB) feature was used to calculate the indoor coordinates of a user. Finally, the optimal user's position was determined by employing loose coupling and coordinate constraints from a magnetic fingerprint map. The findings indicated that the indoor positioning accuracy could reach 0.5 to 0.7 m and that different brands and models of mobile devices could achieve the same accuracy.

摘要

随着定位技术的不断进步，人们对移动设备的使用大幅增加。全球导航卫星系统（GNSS）提高了室外定位性能。但是，由于信号屏蔽效应，它无法有效定位室内用户。常见的室内定位技术包括无线电频率、图像视觉和行人航位推算。然而，每种技术的优缺点使得单一的室内定位技术无法解决各种环境因素相关的问题。在本研究中，提出了一种混合方法，通过将视觉同时定位与地图构建（VSLAM）与磁指纹图相结合，提高室内定位的准确性。智能手机被用作实验设备，内置摄像头和磁传感器用于收集室内环境特征的数据，并确定磁场对建筑物结构的影响。首先，通过使用预先建立的室内磁指纹图，使用加权 k-最近邻匹配方法获得初始位置。然后，结合 VSLAM，使用定向 FAST 和旋转 BRIEF（ORB）特征计算用户的室内坐标。最后，通过使用磁指纹图的松散耦合和坐标约束来确定最佳用户位置。研究结果表明，室内定位精度可达 0.5 到 0.7 米，不同品牌和型号的移动设备可以达到相同的精度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9186/9738752/fc05296c44d8/sensors-22-09244-g013.jpg
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本文引用的文献

1
Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera.基于惯性测量单元传感器和智能手机摄像头的混合室内定位。
Sensors (Basel). 2019 Nov 21;19(23):5084. doi: 10.3390/s19235084.
2
Faster and better: a machine learning approach to corner detection.更快更好：一种用于角点检测的机器学习方法。
IEEE Trans Pattern Anal Mach Intell. 2010 Jan;32(1):105-19. doi: 10.1109/TPAMI.2008.275.
3
Robust structure from motion estimation using inertial data.利用惯性数据进行鲁棒的运动结构估计。
J Opt Soc Am A Opt Image Sci Vis. 2001 Dec;18(12):2982-97. doi: 10.1364/josaa.18.002982.