结合统计滤波与密度聚类的矿山地面点云提取算法

Mine Ground Point Cloud Extraction Algorithm Based on Statistical Filtering and Density Clustering

为了有效提取地面点云并提高运算效率,提出了一种结合统计滤波与密度聚类的矿山地面点云提取算法。首先,基于高效的KD-tree索引算法与统计特征思想进行统计特征的改进,并分析非地面点的空间分布特性;其次,结合二维特征密度空间的分布特性对密度空间进行聚类并分别提取地面点;最后,对各密度空间的提取结果进行求交,即可得到有效地面点。该方法的算法复杂度为o(n^2)。实验表明:该算法具有较高的提取精度和效率;经测试,当近邻点为36时效果最好,总误差为0.00770,均方差为0.019633;同时,对510519个点的提取时间少于27s,约为传统方法耗时的1/7。此外选择了大面积矿山点云对该算法的普适性进行了验证。

We propose a mine ground point cloud extraction algorithm that combines statistical filtering and density clustering to effectively extract ground point clouds and improve the operational efficiency.First,we improve the statistical features based on an efficient KD-tree index algorithm and statistical features,and analyze the spatial distribution characteristics of non-ground points.We then cluster the density space and extract the ground points based on the distribution characteristics of two-dimensional characteristic density space.Lastly,the effective ground points are obtained by intersecting the extracted results of each density space,and the algorithm complexity is observed to be o(n^2).Experiments demonstrate that the proposed algorithm has high extraction accuracy and efficiency.The test indicates that when the neighborhood point value is 36,the effect is the best,with a total error of 0.00770 and a mean square error of 0.019633.Meanwhile,the extraction and calculation time of 510519 points are less than 27 s,which is approximately 1/7 of the time required by traditional methods.In addition,we select a large-area mine point cloud to verify the universality of the algorithm.