为了快速有效地获取散乱点云中的边界特征点和边界线,提出了一种融合改进场力和判定准则的点云特征规则化算法。利用改进的k-d(k-dimensional)树搜索k邻域,以采样点及其k邻域为参考点集拟合微切平面并向该平面投影,在微切平面上建立局部坐标系以将三维坐标转化成二维坐标,利用场力和判定准则识别边界特征点;依据矢量偏转角度和距离对边界特征点进行排序连接;通过改进的三次B样条拟合算法对边界线进行平滑拟合。实验结果表明,该算法能够快速有效地提取边界特征点,且拟合后的边界线偏差量级为10 ^-5 m,具有较高的精度。

In order to obtain the boundary feature points and boundary lines quickly and efficiently in the scattered point cloud, a point cloud feature regularization algorithm is proposed by means of the fusion of improved field force and judging criterion. An improved k-d (k-dimensional) tree method is first used to search the k neighbors of a sampling point. Then this sampling point and its k neighbors are used as the reference points to fit a micro-cut plane and project to this plane. The local coordinate system is established on the micro-cut plane and the three-dimensional coordinate is transformed into the two-dimensional coordinate. The boundary feature points are identified by use of field force and judging criterion. These boundary feature points are sorted and connected according to the vector deflection angle and distance. The boundary lines are smoothed by the improved cubic B-spline fitting algorithm. The experimental results show that the proposed algorithm can used to extract the boundary feature points quickly and efficiently, and the deviations of the fitted boundary lines are in the level of 10 -5 m, indicating a relatively high precision.