标题:An improved PDR indoor locaion algorithm based on probabilistic constraints
作者:You, Yiming ;Zhang, Ting ;Liu, Ye ;Lu, Yuefeng ;Chu, Xiaorong ;Feng, Chen ;Liu, Shuo
通讯作者:Lu, Yuefeng
作者机构:[You, Yiming ;Zhang, Ting ;Liu, Ye ;Lu, Yuefeng ;Chu, Xiaorong ;Feng, Chen ;Liu, Shuo ] Shandong University of Technology, School of Civil and Archite 更多
会议名称:ISPRS Geospatial Week 2017
会议日期:September 18, 2017 - September 22, 2017
来源:International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
出版年:2017
卷:42
期:2W7
页码:181-185
DOI:10.5194/isprs-archives-XLII-2-W7-181-2017
摘要: © Authors 2017. CC BY 4.0 License.">In this paper, we proposed an indoor pedestrian positioning method which is probabilistic constrained by "multi-target encounter" when the initial position is known. The method is based on the Pedestrian Dead Reckoning (PDR) method. According to the PDR method of positioning error size and indoor road network structure, the buffer distance is determined reasonably and the buffer centering on the PDR location is generated. At the same time, key nodes are selected based on indoor network. In the premise of knowing the distance between multiple key nodes, the forward distance of pedestrians which entered from different nodes can be calculated and then we sum their distances and compared with the known distance between the key nodes, which determines whether pedestrians meet. When pedestrians meet, each two are seen as a cluster. The algorithm determines whether the range of the intersection of the buffer meet the conditions. When the condition is satisfied, the centre of the intersection area is taken as the pedestrian position. At the same time, based on the angle mutation of pedestrian which caused by the special structure of the indoor staircase, the pedestrian's location is matched to the real location of the key landmark (staircase). Then the cumulative error of the PDR method is eliminated. The method can locate more than one person at the same time, as long as you know the true location of a person, you can also know everyone's real location in the same cluster and efficiently achieve indoor pedestrian positioning.
© Authors 2017. CC BY 4.0 License.
收录类别:EI
资源类型:会议论文
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