为从海量激光扫描点云数据中准确提取特征,提出了一种基于形态学梯度的激光扫描点云特征提取方法。该方法首先生成海量激光扫描点云数字高程模型,而后通过数学形态学对梯度的定义求取各激光脚点梯度,将梯度局部邻域均值作为局部自适应阈值,对点云数据进行分割,生成特征部分与平坦部分。使用随机抽样一致方法拟合平坦部分平面以及特征部分的圆孔,求取台阶面高度、圆孔内径等特征信息。实验结果表明:该方法可以有效地提取大规模点云数据的特征,圆孔类特征值提取最大误差不超过0.05 mm,台阶面高度提取误差不超过0.1 mm。

To accurately extract the features from massive laser scanning point cloud data, a feature extraction method of laser scanning point cloud based on morphological gradients is proposed. The method first generates the digital elevation model of massive laser scanning point cloud, and then obtains the gradient of each laser footprint by mathematical morphology theory defined by mathematical morphology. The mean value of gradient local nearest points is used as local adaptive threshold. The point cloud data is divided. The characteristic part and the flat part are generated. The random sample consensus method is used to fit the plane from flat part and circles from characteristic part, then the characteristic information such as the height of the step and the radius of the hole is obtained. The experimental results show that the proposed method can effectively extract the features of massive point cloud data. The maximum error of the circles' radius is not more than 0.05 mm, and the minimum error of the step height is not more than 0.1 mm.