为了有效剔除地面激光扫描数据中的大范围密集噪声,同时保留建筑物边缘特征,提出了一种基于距离变化并融合点云强度与密度信息的去噪方法。分析了噪声空间分布特征和点云强度分布,基于水平角和竖直角建立空间四叉树索引,在叶子节点内基于点前后间距特征实现局部点的快速聚类和孤立噪声剔除,在同类点集中基于不同类别强度点数的比值剔除大范围密集噪声。研究结果表明,所提算法能够有效剔除地面激光扫描数据中存在的大范围密集噪声,精度达90%以上。

To remove the large-scale and dense noise from the terrestrial laser scanning data and keep the edge features of buildings, a filtering method fusing intensity with density of points is proposed based on the varied distance of the points to the scanning stations. The spatial distribution of noise and the intensity distribution of point clouds are analyzed comprehensively. The spatial quadtree index is established based on the horizontal and vertical angles. The fast clustering of local points and the removal of isolated points are realized based on the account of the distance before and after points in the leaf nodes, and the large-scale and dense noise is filtered out according to the ratio among different types of intensity point numbers in the same point set. The research results show that the proposed method can be used to effectively filter out the large-scale and dense noise involved in the terrestrial laser scanning data with an accuracy of above 90%.