阶梯垛型点云边界快速提取方法研究

目的 针对阶梯垛型点云边界检测中，现有边界提取算法效率不高的问题，提出一种适用于阶梯垛型点云的快速边界提取算法。方法 首先使用Kd–Tree对点云数据进行拓扑关系构建，其次搜索采样点及其近邻点，构建单位平面向量，并进行向量叠加，根据叠加后的向量模长来判断是否为候选边界点，然后使用近邻点最大夹角法来进行边界点精提取，最后使用Kinect相机获取阶梯垛型点云数据，进行算法对比验证。结果 实验结果表明，文中算法在保证垛型边界提取精度的同时，比经典边界提取算法的运行时间缩短了75.14%，比优化前的边界提取算法运行时间缩短了11.06%。结论 提出的边界提取算法能够快速准确地提取阶梯垛型点云边界，可为自动码垛系统的设计提供参考。

Fast Boundary Extraction Method of Stepped Stack Point Cloud

The work aims to propose a fast boundary extraction algorithm applicable to stepped stack point cloud, so as to solve the problem of low efficiency of existing boundary extraction algorithm in stepped stack point cloud boundary detection. Firstly, Kd-Tree was used to construct the topological relationship of point cloud data. Then, the sampling point and its adjacent point were searched to construct the unit plane vector and perform vector superposition. The candidate boundary point was judged according to the vector modulus length after superposition. Finally, the maximum angle method of adjacent points was used to extract the boundary point accurately. Kinect camera was applied to obtain the stepped stack point cloud data to verify the algorithm. The experimental results showed that the proposed algorithm could shorten the running time by 75.14% compared with the classical boundary extraction algorithm when ensuring the extraction accuracy of stack boundary, and shorten the running time by 11.06% compared with the boundary extraction algorithm before optimization. The proposed boundary extraction algorithm can quickly and accurately extract the stepped stack point cloud boundary, which can provide reference for the design of automatic stack system.