一种散乱分层点云的有序化精简方法 |
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作者姓名: | 解则晓 刘静晓 潘成成 张梦泽 |
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基金项目: | 国家自然科学基金项目(61571408) |
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摘 要: | 针对激光扫描仪所得点云散乱分层的特点,提出一种有序化的精简方法。首先基
于已知标记点建立三维R-tree 和八叉树集成的空间索引,快速准确地获取局部点云数据,保证
良好的数据检索效率。然后根据局部点云数据的参考平面法向量信息,选取工件坐标系中的一
个坐标轴作为参数化的方向,对局部点云数据进行参数化并拟合二次曲面。最后对R-tree 叶节
点内的二次曲面进行有序化采样,使散乱分层的点云变为单层,得到整个型面的有序参考点集。
应用实例表明,该方法适用于大规模的、具有复杂几何特征且存在一定程度散乱分层的点云,
可以有效地提高数据点的整体精确度,且不会丢失点云的细节特征,具有较强的实用性。
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关 键 词: | 数据精简 有序化 散乱分层点云 标记点 3DR-tree |
A Data Reduction and Ordering Algorithm for Scattered and
Layered Point Cloud |
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Authors: | Xie Zexiao Liu Jingxiao Pan Chengcheng Zhang Mengze |
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Abstract: | Concerning the scattered and layered characteristic of point cloud acquired by laser
scanners, a data reduction and ordering algorithm is proposed. Firstly the spatial index of point cloud
is created based on known marked points using a method integrating Octree and 3D R-tree, ensuring
fast and correct access to local data and high efficiency of data retrieval. Secondly one axis of the
work coordinate system is selected as the projective direction for parameterizing the local data, which
is determined by the normal vector of local reference plane. Then along the selected direction the
local data is parameterized and the quadratic surface is approximated. Finally the ordered set of
reference points is obtained by sampling the quadratic surface through the R-tree’s leaf nodes, making
the scattered and layered point cloud be single layered. Application examples show that the algorithm
can improve the overall accuracy of the data as well as maintain the details of point cloud, indicating
good validity and practicability in the reduction of scattered and layered large-scale point cloud with
complex geometric features. |
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Keywords: | data reduction spatial ordering scattered and layered point cloud marked point 3D R-tree |
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