复杂采空区激光多点探测及点云拼接与精简

Point cloud merging and compression of complicated goaf using multi-point laser-scan

针对复杂采空区激光探测中存在探测“盲区”和点云数据分布不均的问题，研究激光多点扫描和点云数据拼接与精简方法.通过多点探测避免了单次探测“盲区”，加密了数据稀疏区.提出了基于公共坐标和最小二乘法的靶标矩阵转换方法，实现了多点探测点云的拼接.统计了点云密集区的分布规律；对密集散乱点云，提出了沿 y 轴方向分层剖分，层内数据以 x和 z 坐标极值分区，区内每点以 x 值排序后依步长筛选的精简算法.大型贯通采空区验证表明：基于最小二乘法的拼接算法最优，误差范围在0.1 mm 左右；数据精简率为15%-25%，确保了边界三维信息的完整性.

In view of the problems of ‘blind spots’ in complicated goaf detecting by using laser scanning and point cloud density distribution inhomogeneity, this article introduced multi-point laser scan and point cloud merging and compression. Multi-point scan in complicated goaf avoided ‘blind spots’ and densified sparse point cloud regions. The merging algorithm of point cloud data was put forward based on a common coordinate system and the least-squares principle to solve the target transformation matrix. After the distri-bution rule of point cloud concentration areas was analyzed, the scattered point cloud compression algorithm was proposed, in which the point cloud was divided into portions along the y direction firstly, then intralayer data were divided by the extreme values of x and z, and each point was sorted on the x value and screened on step δ. Error analysis of an instance of large versed goaf shows that the merging algorithm based on the least-squares principle will achieve high precision with an error range of about 0. 1 mm. The compres-sion algorithm can achieve a compression proportion of 15% to 25% and ensure the integrity of 3D boundary information at the same time.