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本文中的点云数据来源于直升机电力巡线航拍的机载激光雷达点云数据,并以las文件格式组织。点的数据组成包括3维坐标信息(经度、纬度和高程值)和其它信息(回波数、分类号等)。实验区段为某±800kV特高压直流输电线路衡阳段,路径长度约5.4km,走廊占地面积约45km2。从中截取一部分区域,共有15813380个点,文件大小为422MB。计算机为普通笔记本电脑,配置为Core i5 2.4GHz、8GB内存、256GB硬盘,软件方面为Windows10 64位操作系统,VS2010编译环境,OSG3维渲染引擎。
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设置不同的八叉树最大叶子节点个数和树高,八叉树建树耗时如表 1所示。由表 1可以看出,基于阈值分割的八叉树构建方较传统八叉树构建方法具有更快的速度,叶子节点中点云分布的平衡性较高,节省了存储空间。在叶子数量和深度适中时,基于阈值分割的八叉树构建方法具有较明显的优势,随着八叉树叶子节点个数增大,树高增加,两种方法在构树耗时上相近。
Table 1. Consuming time of different octree leaf nodes and height
point number D leaf nodes number N tree height H octree build in this paper/s octree build in tradition/s 15813380 1200 6 2 10 15813380 1400 6 2.5 12 15813380 1600 6 11 18 15813380 1800 8 15 19 15813380 2000 8 20 21 15813380 2200 8 25 28 在此基础上,采用MPI并行化编程模型可以再一次提升构树速度,如图 6所示。3条曲线分别表示不同进程数,横坐标为构建八叉树的最大叶节点个数N,纵坐标为八叉树构建耗时t。比较MPI环境下,单进程、四进程和八进程的执行速度,可以看到, 基于并行计算的方法,随着开辟的进程数增加,八叉树构建速度可以得到很好的提高。
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以数字高程模型和高分辨率遥感影像构建的输电线路走廊3维地理环境为基础,加载电力巡线直升机激光点云数据,可以一次性载入并流畅显示。帧数在25左右。海量点云3维可视化如图 7所示。
电力巡线直升机激光扫描数据的高效组织与显示
Effective organization and visualization of helicopter-based laser scanning data in power line inspection
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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。Abstract: 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.
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Table 1. Consuming time of different octree leaf nodes and height
point number D leaf nodes number N tree height H octree build in this paper/s octree build in tradition/s 15813380 1200 6 2 10 15813380 1400 6 2.5 12 15813380 1600 6 11 18 15813380 1800 8 15 19 15813380 2000 8 20 21 15813380 2200 8 25 28 -
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