激光与光电子学进展, 2020, 57 (2): 021107, 网络出版: 2020-01-03
结合统计滤波与密度聚类的矿山地面点云提取算法 
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Mine Ground Point Cloud Extraction Algorithm Based on Statistical Filtering and Density Clustering
图 & 表
图 2. 噪声的空间分布。(a)平面点云图; (b)邻点的距离图
Fig. 2. Spatial distribution of noise. (a) Plane point cloud map; (b) distance map of neighbor points
图 4. 邻域10个点的特征密度分布
Fig. 4. Characteristic density distributions of 10 points in neighborhood
图 5. 邻域20个点的特征密度分布
Fig. 5. Characteristic density distributions of 20 points in neighborhood
图 6. 邻域30个点的特征密度分布
Fig. 6. Characteristic density distributions of 30 points in neighborhood
图 7. 邻域40个点的特征密度分布
Fig. 7. Characteristic density distributions of 40 points in neighborhood
图 8. 提取效果。(a)原始云点;(b) 10个邻域点;(c) 20个邻域点;(d) 30个邻域点;(e) 40个邻域点
Fig. 8. Extracted results. (a) Original point cloud; (b) 10 neighbor points; (c) 20 neighbor points; (d) 30 neighbor points; (e) 40 neighbor points
图 9. 实验结果及精度。(a)斜率差的变化;(b)精度差的变化;(c)地面点云个数;(d)误差曲线
Fig. 9. Experimental results and accuracy. (a) Trend of slope difference; (b) trend of accuracy difference; (c) number of ground point clouds; (d) error curves
图 10. 半径滤波、体素滤波及统计滤波提取效果
Fig. 10. Extraction results of radius filter, voxel filter, and statistical filter
图 11. 基于方法库的提取效果。(a)去噪正面效果;(b)去噪侧面效果
Fig. 11. Extraction results based on Method-Library. (a) After denoising in front view; (b) after denoising in side view
表 1不同邻域点个数下的提取结果
Table1. Extraction results under different numbers of neighbor points
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表 2其他算法提取结果
Table2. Extraction results of other algorithms
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杨鹏, 刘德儿, 刘靖钰, 张荷苑. 结合统计滤波与密度聚类的矿山地面点云提取算法[J]. 激光与光电子学进展, 2020, 57(2): 021107. Yang Peng, Liu Deer, Liu Jingyu, Zhang Heyuan. Mine Ground Point Cloud Extraction Algorithm Based on Statistical Filtering and Density Clustering[J]. Laser & Optoelectronics Progress, 2020, 57(2): 021107.
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