一种散乱分层点云的有序化精简方法

A Data Reduction and Ordering Algorithm for Scattered and Layered Point Cloud

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。

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.