自适应α-shapes平面点云边界提取方法

Adaptive Alpha-shapes plane point cloud boundary extraction method

针对基于微切面的点云边界提取方法在LiDAR点云边界提取中效率低,难以保证边界提取的精细度和完整性问题,提出了一种可调节滚动圆半径的α-shapes平面点云边界提取算法.该算法首先将点云数据栅格化,排除非边界点,并通过计算P点的K个邻近点平均距离和增设调节因子,设置滚动圆半径α,最后采用α-shapes算法提取点云边界.对近邻K值、点云形状和点云密度等分析,证明近邻K值与调节因子ω之间具有函数关系,及调节因子与点云密度和点云形状无关的结论.结果证明:该算法在准确提取点云边界情况下,能够快速提取完整点云边界,提高后续点云重建速度与效率,该算法具有良好的稳健性.

Aiming at improving the efficiency of point cloud boundary extraction method, based on micro-cut surface in LiDAR point cloud,and to ensure the fineness and integrity of boundary extraction,an α-shapes algorithm of adjustable rolling circle radius is proposed to deal with plane point cloud boundary, The algorithm firstly rasterized point cloud data and then excluded non-boundary points.Thirdly, rolling round radius was set by a regulatory factor and the average distance between K adjacent points of P. Finally,α-shapes algorithm was utilized to extract the point cloud boundary. To analyze the relationship among the k nearest neighbors,point cloud shape and point cloud density,it is proved that there is a functional relationship between the K value and the regulating factor ω. And the regulatory factor is independent of point cloud density and point cloud shape. The results showed that this algorithm can quickly extract the complete point cloud boundary and improve the speed and efficiency of subsequent point cloud reconstruction under the condition of accurately extracting the point cloud boundary, with good robustness.