电力巡线直升机激光扫描数据的高效组织与显示
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摘要
为了提高激光扫描数据的后处理速度和显示效率,采用了并行化阈值分割构建八叉树结构、对海量点云进行分块处理的方法。基于高差的数据抽稀方法,逐层精简八叉树叶节点中的数据,保存在八叉树外存结构中,构建点云多分辨率细节层次模型。采用视点变化与分页数据库结合的内外存调度策略,对一组电力巡线直升机获取的激光扫描点云数据进行实验验证。结果表明,该方法在八叉树构建速度和海量点云数据显示效率上的优越性,可以很好地满足电力巡线的时效性需求。
In order to improve the processing speed and display efficiency of laser scanning data, octree structure was constructed by parallel threshold segmentation and massive point clouds were processed in blocks. The data in octree leaf nodes was simplified by data thinning method based on height difference layer by layer and was stored in octree external memory structure. The multi-resolution level of detail model of point cloud was constructed. The internal and external memory scheduling strategy based on view change and paging database was adopted. The laser scanning point cloud data acquired by a group of power line patrol helicopters were experimentally validated. The results show that this method has advantages of octree construction speed and display efficiency of large amount of point cloud data. It can satisfy the timeliness requirement of power line patrol very well.
In order to improve the processing speed and display efficiency of laser scanning data, octree structure was constructed by parallel threshold segmentation and massive point clouds were processed in blocks. The data in octree leaf nodes was simplified by data thinning method based on height difference layer by layer and was stored in octree external memory structure. The multi-resolution level of detail model of point cloud was constructed. The internal and external memory scheduling strategy based on view change and paging database was adopted. The laser scanning point cloud data acquired by a group of power line patrol helicopters were experimentally validated. The results show that this method has advantages of octree construction speed and display efficiency of large amount of point cloud data. It can satisfy the timeliness requirement of power line patrol very well.
引文
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[3] GUO B,HUANG X F,ZHANG F,et al.Points cloud classification using jointboost combined with contextual information for feature reduction[J].Acta Geodaeticaet Cartographica Sinica,2013,42(5):715-721(in Chinese).
[4] HOOPER B.Vegetation management takes to the air[J].Transmi-ssion & Distribution World,2003,55(9):TD-67-CW.
[5] AHMAD J,MALIK A S,XIA L K,et al.Vegetation encroachment monitoring for transmission lines right-of-ways:A survey[J].Electric Power Systems Research,2013,95(1):339-352.
[6] LIN X G,ZHANG J X.3-D power line reconstruction from airborne lidar point cloud of overhead electric power transmission corridors[J].Acta Geodaeticaet Cartographica Sinica,2016,45(3):347-353(in Chinese).
[7] GUO B,HUANG X F,LI Q Q,et al.A stochastic geometry method for pylon reconstruction from airborne lidar data[J].Remote Sensing,2016,8(3):243.
[8] LI Q Q,CHENG Zh P,HU Q W.A model-driven approach for 3-D modeling of pylon from airborne lidar data[J].Remote Sensing,2015,7(9):11501-11524.
[9] YU J,MU Ch,FENG Y,et al.Powerlins extraction techniques from airborne lidar data[J].Geomatics and Information Science of Wuhan University,2011,36(11):1275-1279(in Chinese).
[10] MAI X M,CHEN Ch,PENG X Y,et al.3-D visualization technique of transmission line corridors:system design and implementation[J].Electric Power,2015,48(2):98-103(in Chinese).
[11] TANG F F,RUAN Zh M,LIU X.Research of filtering method for urban airborne LIDAR data[J].Laser Technology,2011,35(4):527-530(in Chinese).
[12] WANG L.Research on organization and viusalization of massive 3D laser point cloud data[D].Beijing:Beijing University of Technology,2016:3-7(in Chinese).
[13] CHEN Ch,WANG K,XU W X,et al.Real-time visualizing of massive vehicle-borne laser scanning point clouds[J].Geomatics and Information Science of Wuhan University,2015,40(9):1163-1168(in Chinese).
[14] ZHI X D,LIN Z J,SU G Z,et al.Research on organization of airborn lidar points cloud based on improved auadtree algorithm[J].Computer Engineering and Application,2010,46(9):71-74(in Chinese).
[15] HUANG X F,TAO Ch,JIANG W Sh,et al.Real time render large amount of lidar point clouds data[J].Geomatics and Information Science of Wuhan University,2005,30(11):38-41(in Chinese).
[16] YAN L,HU X B,XIE H.Data management and visualization of mobile laser scanning point cloud[J].Geomatics and Information Science of Wuhan University,2017,42(8):1131-1136(in Chin-ese).
[17] QIU Y.The research on the real-time rendering of three-dimensional laser scanning point cloud based on out-of-core octree[D].Tianjin:Tianjin Normal University,2017:20-22(in Chinese).
[18] YANG Zh F,WAN G,LI F,et al.Research on rendering of ma-ssive point cloud based on multi-resolution LOD[J].Geospatial Information,2016,14(10):22-25(in Chinese).
[19] WANG L,GUO Q J,JIANG H.A new LOD method based on an improved octree index structure for the visualization of massive point cloud[J].Computer Engineering & Software,2016,37(3):114-117(in Chinese).
[20] RIZKI P N M,PARK J,OH S,et al.STR-octree indexing method for processing LiDAR data[C]//2015 IEEE Sensors.New York,USA:IEEE,2016:1-4.
[21] XIE H,WU B Y,ZHAO Zh.A novel organization method of ma-ssive point cloud[J].Remote Sensing Information,2013,28(6):26-32(in Chinese).
[22] ELSEBERG J,BORRMANN D,NüCHTER A.One billion points in the cloud-an octree for efficient processing of 3-D laser scans[J].ISPRS Journal of Photogrammetry & Remote Sensing,2013,76(1):76-88.
[23] LIANG Y L.Research on massive mehicular point cloud data organization and rapid visualization technology[D].Beijing:Capital Normal University,2012:14-20(in Chinese).
[24] KREYLOS O,BAWDEN G W,KELLOGG L H.Immersive Visua-lization and Analysis of LiDAR Data[C]// International Symposium on Advances in Visual Computing.New York,USA:Springer-Verlag,2008:846-855.