首页 | 本学科首页   官方微博 | 高级检索  
     

基于WiFi和WSN的室内外混合定位研究
引用本文:方志祥, 姜宇昕, 管昉立. 融合可视与不可视地标的行人相对定位方法[J]. 武汉大学学报 ( 信息科学版), 2021, 46(5): 601-609. DOI: 10.13203/j.whugis20190411
作者姓名:方志祥  姜宇昕  管昉立
作者单位:1.武汉大学测绘遥感信息工程国家重点实验室,湖北 武汉,430079;2.自然资源部第一大地测量队,陕西 西安,410083
基金项目:国家自然科学基金(41771473)
摘    要:
充分考虑不同数据源在变化场景下的数据差异性和行人在导航定位服务中的空间认知习惯,提出了一种融合可视地标与不可视地标的行人相对定位方法。利用基于传感器复合证据理论的方法构建目标路径的不可视地标(如磁场变化、WiFi更新等),检测GoPro Fusion设备获取的全景影像中的视觉显著的可视地标及其与采样点间的相对空间方位属性;根据行人实时获取的传感器数据和地标方位信息分别推估行人在目标路径中可能停留的路段区域;采用贝叶斯概率融合方法融合可视地标与不可视地标数据进行行人定位结果推估。
实验结果表明,融合多源数据可以解决单一场景下行人定位精度不足的问题。在传感器特征较少的单一场景下,与基于不可视地标的行人定位方法相比,该方法的精度提升了12.78%。


关 键 词:地标  相对定位法  传感器  全景影像  贝叶斯概率融合
收稿时间:2020-08-28

Non-GPS Navigation for Se-curity Personnel and First Responders
FANG Zhixiang, JIANG Yuxin, GUAN Fangli. Pedestrian Relative Positioning Method Based on Visible and Invisible Landmarks[J]. Geomatics and Information Science of Wuhan University, 2021, 46(5): 601-609. DOI: 10.13203/j.whugis20190411
Authors:FANG Zhixiang  JIANG Yuxin  GUAN Fangli
Affiliation:1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;2.The First Geodetic Surveying Brigade, Ministry of Natural Resources, Xi'an 410083, China
Abstract:
  Objectives  Visible landmarks and invisible landmarks are important aids for research into and design of applications for some target route.  Methods  We propose a pedestrian relative positioning method by fusing the visible landmarks and invisible landmarks, considering the data variance in different environ‐ ments and the pedestrian customary behavior during pedestrian positioning. Firstly, the invisible landmarks (e.g., magnetic changes, WiFi(wireless fidelity) updates) along the target route are detected by smartphone sensors, and the evidence framework is built by segmenting the target route with data characteristics of sen‐ sors. Then the salient visible landmarks could be detected, and the relative spatial relations between land‐ marks and panoramas could be derived based on their coordinates.Secondly, the probability values of pedes‐ trians in the road segments are respectively obtained, based on the similarly of the real‐time sensor data and sensor data from each of the evidence framework.
And the relative azimuth relations of landmarks in the panoramas could be updated instantly. Finally, based on the Bayesian probability fusion method, the pedes‐ trian positioning results could be computed through fusing the results of sensors and panoramas.In detail, the probability values of pedestrians in the road segments will be recalculated based on the panoramic image results.
  Results  The experimental results demonstrate that the proposed method could improve the posi‐ tioning accuracy in a single pedestrian walking environment by fusing multi‐source data.In an environment with fewer sensor features, the accuracy achieved by this method increases by 12.78%, which is higher than that of the invisible landmark‐based method.  Conclusions  The pedestrian relative positioning method not only solves the problems of sensor instability and less sensor features, but also improves the positioning accuracy.
Keywords:landmark  relative positioning method  sensor  panorama  Bayesian probability fusion
点击此处可从《武汉大学学报(信息科学版)》浏览原始摘要信息
点击此处可从《武汉大学学报(信息科学版)》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号