基于地面三维激光扫描仪获取树木的点云数据,提出了一种通过细化点云数据体素生成树木骨架的方法。基于树木点云数据构建体素空间,计算点云体素坐标;根据各体素中包含点云数据的统计信息进行体素噪点滤除;利用细化模板对滤除噪声的体素进行细化处理,基于细化后的体素拟合出骨架节点;根据树木在自然空间上的连通性和深度优先搜索算法连接骨架节点,生成树木骨架。利用一棵银杏树和一棵重瓣榆叶梅树对算法进行验证。采用地面三维激光扫描仪分别对这两棵树进行扫描,基于不同扫描精度的树木点云,分析了不同参数对树木骨架生成的影响。在生成银杏树骨架时,本文方法运行时间约缩短至GSA方法的1/30。而处理数据量更大的重瓣榆叶梅树点云数据时,树木骨架生成时间更是缩短至GSA方法的1/67。实验结果表明,所提算法生成的两棵树木骨架形态与树木原始点云所表现的形态结构相对一致,并且具有较好的运算效率,该算法具有一定的可行性和有效性。

A method for generating tree skeletons by thinning the voxels of point cloud data has been proposed based on point cloud data acquired by a terrestrial three-dimensional laser scanner. First, the voxel space is constructed based on the point cloud data of a tree, and the voxel coordinates of point clouds are calculated simultaneously. Second, the noise points in the voxels are filtered according to the statistical information of point cloud data in each voxel. Third, the voxels without noise are thinned using thinning templates. The skeleton nodes are then fitted considering the thinned voxels. Finally, based on the connectivity of natural trees, a depth-first search algorithm is employed to connect nodes and generate tree skeletons. The proposed method is tested with a ginkgo tree and an Amygdalus triloba f. multiplex tree. The two trees are scanned by a terrestrial three-dimensional laser scanner. The effects of different parameters on the tree skeleton are analyzed by using the tree point clouds with different scanning accuracy. In comparison with the GSA method, the proposed method can reduce time consumption for the tree skeleton generation of the ginkgo tree and the Amygdalus triloba f. multiplex tree to 1/30 and 1/67, respectively. Experimental results show that the skeletons of the two trees generated by the proposed method are consistent with the original point clouds,and the proposed method is feasible and efficient.