一种室内导航网络众包构建方法
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摘要
室内导航网络是室内位置服务的基础,传统人工测绘或基于CAD半自动提取等方法时效性较差。室内移动对象众包轨迹数据的出现为室内导航网络构建提供了一种新的解决方案。提出一种室内导航网络众包构建方法。首先提取出用于构建室内导航网络的廊道区域轨迹点;其次通过轨迹点生长融合聚类算法将廊道轨迹点转化为聚类点;最后通过聚类点连接生成室内导航网络。以某商城一楼2 d的移动对象轨迹数据进行了实验。结果表明,本文方法提取的室内导航网络准确度较高,能够为室内空间结构快速变化检测和更新提供支持。
Indoor navigation network is the basis of indoor location service. Traditional manual mapping or semi-automatic extraction based on CAD are not efficient. The emergence of crowdsourcing trajectory data for indoor mobile objects provides a new solution for the construction of indoor navigation network. A crowdsourcing method for indoor navigation network is presented in the paper. Firstly, the corridor trajectory points are extracted to construct the indoor navigation network. Secondly, the corridor trajectory points are transformed into clustering points by the fusion clustering algorithm of trajectory point growth. Finally, the indoor navigation network is generated by the connection of clustering points. The experiment was carried out with the trajectory data of moving objects on the first floor of a shopping mall for two days. The results show that the accuracy of road network extracted by using this method is high, and the experimental results can provide data support for rapid change detection and update of indoor spatial structure.
Indoor navigation network is the basis of indoor location service. Traditional manual mapping or semi-automatic extraction based on CAD are not efficient. The emergence of crowdsourcing trajectory data for indoor mobile objects provides a new solution for the construction of indoor navigation network. A crowdsourcing method for indoor navigation network is presented in the paper. Firstly, the corridor trajectory points are extracted to construct the indoor navigation network. Secondly, the corridor trajectory points are transformed into clustering points by the fusion clustering algorithm of trajectory point growth. Finally, the indoor navigation network is generated by the connection of clustering points. The experiment was carried out with the trajectory data of moving objects on the first floor of a shopping mall for two days. The results show that the accuracy of road network extracted by using this method is high, and the experimental results can provide data support for rapid change detection and update of indoor spatial structure.
引文
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[3] 武恩超,张恒才,吴升.基于中轴变换算法的室内外一体化导航路网自动生成方法[J].地球信息科学学报,2018,20(6):22-29.WU E C,ZHANG H C,WU S.An automatic generation method of indoor and outdoor integrated navigation network based on mid-axis transformation algorithm[J].Journal of Geo-Information Science,2018,20(6):22-29.
[4] 刘智伟,李建胜,王安成,等.基于运动捕捉系统的UWB室内定位精度标定方法[J].测绘科学技术学报,2017,34(2):147-151.LIU Z W,LI J S,WANG A C,et al.UWB indoor positioning accuracy calibration method based on motion capture system[J].Journal of Geomatics Science and Technology,2017,34(2):147-151.
[5] 王培晓,王海波,傅梦颖,等.室内用户语义位置预测研究[J].地球信息科学学报,2018,20(12):1689-1698.WANG P X,WANG H B,FU M Y,et al.Semantic location prediction of indoor users[J].Journal of Geo-Information Science,2018,20(12):1689-1698.
[6] ARONOV B,DRIEMEL A,KREVELD M V,et al.Segmentation of trajectories on nonmonotone criteria[J].ACM Transactions on Algorithms,2015,12(2):1-28.
[7] LEE J G,HAN J,WHANG K Y.Trajectory clustering:A partition-and-group framework[C]//ACM SIGMOD International Conference on Management of Data.Beijing,2007:593-604.
[8] BIAGIONI J,ERIKSSON J.Map inference in the face of noise and disparity[C]//International Conference on Advances in Geographic Information Systems.Redondo Beach,California,2012:79-88.
[9] DAVIES J J,BERESFOR A R,HOPPER A.Scalable,distributed,real-time map generation[J].IEEE Pervasive Computing,2006,5(4):47-54.
[10] 杨伟,艾廷华.基于车辆轨迹大数据的道路网更新方法研究[J].计算机研究与发展,2016,53(12):2681-2693.YANG W,AI T H.Research on road network renewal method based on vehicle trajectory big data[J].Computer Research and Development,2016,53(12):2681-2693.
[11] LI J,QIN Q,XIE C,et al.Integrated use of spatial and semantic relationships for extracting road networks from floating car data[J].International Journal of Applied Earth Observations & Geoinformation,2012,19(1):238-247.
[12] TANG L,REN C,LIU Z,et al.A road map refinement method using delaunay triangulation for big trace data[J].ISPRS International Journal of Geo-Information,2017,6(2):45.
[13] CHEN D,GUIBAS L J,HERSHBERGER J,et al.Road network reconstruction for organizing paths[C]//ACM-Siam Symposium on Discrete Algorithms.Austin,Texas,2010:1309-1320.
[14] AHMED M,KARAGIORGOU S,PFOSER D,et al.A comparison and evaluation of map construction algorithms using vehicle tracking data[J].Geoinformatica,2015,19(3):601-632.
[15] 孔月萍,万晨,张跃鹏,等.栅格数据中面状地物的骨架线提取方法[J].测绘科学技术学报,2017,34(3):311-314.KONG Y P,WAN C,ZHANG Y P,et al.Skeleton line extraction method of surface objects in grid data[J].Journal of Geomatics Science and Technology,2017,34(3):311-314.
[16] BIRANT D,KUTA.ST-DBSCAN:An algorithm for clustering spatial-temporal data[J].Data & Knowledge Engineering,2007,60(1):208-221.
[17] JIN P,CUI T,WANG Q,et al.Effective similarity search on indoor moving-object trajectories[M].Switzerland:Springer International Publishing,2016:181-197.