提出了一种高效的基于八叉树体素自适应生成与体素分层次生长的平面提取方法, 其主要思路为采用体素信息统计的方式进行相关阈值参数的自动选定, 以及基于体素的生长替代基于点的生长进行平面提取。首先, 对点云进行八叉树初始剖分并计算其几何属性信息(包括法矢、特征值以及维度特征描述符等); 然后, 通过统计得到细分终止条件, 并对初始八叉树进行进一步自适应剖分, 得到一系列非均匀八叉树体素; 最后, 在体素层面进行区域生长阈值的统计与体素的分层次生长, 进行点云平面的精细提取。利用4种不同类型的点云数据对本文算法进行了测试。实验结果显示: 精度和召回率可以达到95%以上, 表明本文算法对数据质量不敏感, 可以自动适应不同平台采集的、不同分布密度和不同数据质量的激光点云, 并且高效地得到精细的点云平面提取结果。

An efficient method for extraction of planar features from point clouds was proposed based on the concepts of self-adaptive octree voxel generation and voxel-based region growing. The proposed method involved the selection of correlated thresholds through statistics of voxel information. A voxel-based region growing approach was employed for planar feature extraction, instead of a point-based one. A point cloud was voxelized in initial voxel width and the geometrical features for each voxel were calculated, including the normal vector, eigenvalue, and three dimensionality features. The terminal constraints for octree subdivision were thereby determined through statistics and a list of octree voxels with inhomogeneous sizes was obtained after subdivision. Furthermore, planar facets were extracted through voxel-based region growing at different levels associated with the corresponding statistical threshold constraints. Evaluation experiments were performed by analyzing four different types of point clouds. The obtained experimental results show that the precision and recall rates can reach 95%, which indicates that the proposed method is insensitive to data quality and can be adaptive to various laser-scanned point cloud data. The proposed method can therefore achieve fine planar feature extraction results with high operating efficiency.