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 下载: 749次

Mine Ground Point Cloud Extraction Algorithm Based on Statistical Filtering and Density Clustering
作者单位
1 江西理工大学建筑与测绘工程学院, 江西 赣州 341000
2 成都大学中国东盟艺术学院, 四川 成都 610106
引用该论文

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.

参考文献

[1] 惠振阳, 程朋根, 官云兰, 等. 机载LiDAR点云滤波综述[J]. 激光与光电子学进展, 2018, 55(6): 060001.

    Hui Z Y, Cheng P G, Guan Y L, et al. Review on airborne LiDAR point cloud filtering[J]. Laser & Optoelectronics Progress, 2018, 55(6): 060001.

[2] 赵京东, 杨凤华, 郭英新. 散乱点云去噪与简化的统一算法[J]. 计算机应用, 2017, 37(10): 2879-2883.

    Zhao J D, Yang F H, Guo Y X. Unified algorithm for scattered point cloud denoising and simplification[J]. Journal of Computer Applications, 2017, 37(10): 2879-2883.

[3] 袁华, 庞建铿, 莫建文. 基于噪声分类的双边滤波点云去噪算法[J]. 计算机应用, 2015, 35(8): 2305-2310.

    Yuan H, Pang J K, Mo J W. Denoising algorithm for bilateral filtered point cloud based on noise classification[J]. Journal of Computer Applications, 2015, 35(8): 2305-2310.

[4] 邓文君, 叶景杨, 张铁. 面向机器人磨抛的激光点云获取及去噪算法[J]. 光学学报, 2016, 36(8): 0814002.

    Deng W J, Ye J Y, Zhang T. Acquisition and denoising algorithm of laser point cloud oriented to robot polishing[J]. Acta Optica Sinica, 2016, 36(8): 0814002.

[5] 李鹏飞, 吴海娥, 景军锋, 等. 点云模型的噪声分类去噪算法[J]. 计算机工程与应用, 2016, 52(20): 188-192.

    Li P F, Wu H E, Jing J F, et al. Noise classification denoising algorithm for point cloud model[J]. Computer Engineering and Applications, 2016, 52(20): 188-192.

[6] 林洪彬, 付德敏, 王银腾. 基于参数自适应各向异性高斯核的散乱点云保特征去噪[J]. 计算机集成制造系统, 2017, 23(12): 2583-2592.

    Lin H B, Fu D M, Wang Y T. Feature preserving denoising of scattered point cloud based on parametric adaptive and anisotropic Gaussian kernel[J]. Computer Integrated Manufacturing Systems, 2017, 23(12): 2583-2592.

[7] 许龙, 黄翔, 李根. 一种基于模糊C均值和均值滤波的点云去噪算法[J]. 机械制造与自动化, 2016, 45(4): 5-7, 23.

    Xu L, Huang X, Li G. Point clouds de-noise algorithm based on fuzzy C-means and mean filter[J]. Machine Building & Automation, 2016, 45(4): 5-7, 23.

[8] 吕娅, 万程辉. 三维激光扫描地形点云的分层去噪方法[J]. 测绘科学技术学报, 2014, 31(5): 501-504.

    Lü Y, Wan C H. A denoising method by layering for terrain point cloud from 3D laser scanner[J]. Journal of Geomatics Science and Technology, 2014, 31(5): 501-504.

[9] 陈凯, 张达, 张元生. 采空区三维激光扫描点云数据处理方法[J]. 光学学报, 2013, 33(8): 0812003.

    Chen K, Zhang D, Zhang Y S. Point cloud data processing method of cavity 3D laser scanner[J]. Acta Optica Sinica, 2013, 33(8): 0812003.

[10] 惠振阳, 胡友健. 基于LiDAR数字高程模型构建的数学形态学滤波方法综述[J]. 激光与光电子学进展, 2016, 53(8): 080001.

    Hui Z Y, Hu Y J. Review on morphological filtering algorithms based on LiDAR digital elevation model construction[J]. Laser & Optoelectronics Progress, 2016, 53(8): 080001.

[11] Chen LZ, Feng BH. Denoising algorithm for bilateral filtered point cloud based on variance threshold[C]∥2018 3rd International Conference on Materials Science, Machinery and Energy Engineering(MSMEE 2018), June 29, 2018, Taiyuan, Shanxi, China. [S.l.: s.n.], 2018.

[12] TaubinG. A signal processing approach to fair surface design[C]∥Proceedings of the 22nd annual conference on Computer graphics and interactive techniques - SIGGRAPH'95, August 6-11, 1995, Los Angeles, CA, USA. New York: ACM, 1995: 351- 358.

[13] Jones T R, Durand F, Desbrun M. Non-iterative, feature-preserving mesh smoothing[J]. ACM Transactions on Graphics, 2003, 22(3): 943-949.

[14] Fleishman S, Drori I, Cohen-Or D. Bilateral mesh denoising[J]. ACM Transactions on Graphics, 2003, 22(3): 950-953.

[15] 王竞雪, 张雪洋, 洪绍轩, 等. 结合形态学和TIN三角网的机载LiDAR点云滤波算法[J]. 测绘科学, 2019, 44(5): 151-156, 183.

    Wang J X, Zhang X Y, Hong S X, et al. Aerial LiDAR point cloud filtering algorithm combining mathematical morphology and TIN[J]. Science of Surveying and Mapping, 2019, 44(5): 151-156, 183.

[16] 邢承滨, 邓兴升, 徐康. 形态学滤波关键参数阈值的等值线确定方法[J]. 激光与光电子学进展, 2019, 56(16): 162802.

    Xing C B, Deng X S, Xu K. Contour determination method for threshold of morphological filtering key parameters[J]. Laser & Optoelectronics Progress, 2019, 56(16): 162802.

[17] 赵明波, 何峻, 田军生, 等. 基于改进的渐进多尺度数学形态学的激光雷达数据滤波方法[J]. 光学学报, 2013, 33(3): 0328001.

    Zhao M B, He J, Tian J S, et al. Ladar data filtering method based on improved progressive multi-scale mathematic morphology[J]. Acta Optica Sinica, 2013, 33(3): 0328001.

[18] 牛晓静, 王美丽, 何东健. 一种聚类与滤波融合的点云去噪平滑方法[J]. 计算机应用与软件, 2016, 33(10): 148-152.

    Niu X J, Wang M L, He D J. A point cloud denoising and smoothing method based on fusion of clustering and filtering[J]. Computer Applications and Software, 2016, 33(10): 148-152.

[19] 苏本跃, 马金宇, 彭玉升, 等. 基于K-means聚类的RGBD点云去噪和精简算法[J]. 系统仿真学报, 2016, 28(10): 2329-2334, 2341.

    Su B Y, Ma J Y, Peng Y S, et al. Algorithm for RGBD point cloud denoising and simplification based on K-means clustering[J]. Journal of System Simulation, 2016, 28(10): 2329-2334, 2341.

[20] 张巧英, 陈浩, 朱爽. 9(6): 101-104, Ⅳ-Ⅴ[J]. . 密度聚类算法在连续分布点云去噪中的应用. 地理空间信息, 2011.

    Zhang Q Y, Chen H. 9(6): 101-104, Ⅳ-Ⅴ[J]. Zhu S. Application of density-based clustering algorithms in noise removing of continuous distributed point clouds. Geospatial Information, 2011.

[21] 田青华, 白瑞林, 李杜. 基于改进欧氏聚类的散乱工件点云分割[J]. 激光与光电子学进展, 2017, 54(12): 121503.

    Tian Q H, Bai R L, Li D. Point cloud segmentation of scattered workpieces based on improved Euclidean clustering[J]. Laser & Optoelectronics Progress, 2017, 54(12): 121503.

[22] 冯东恒, 石波, 卢秀山, 等. 一种顾及水下地形特点的多波束点云去噪算法[J]. 测绘科学技术学报, 2017, 34(4): 364-369.

    Feng D H, Shi B, Lu X S, et al. A multi-beam point cloud denoising algorithm considering underwater topographic features[J]. Journal of Geomatics Science and Technology, 2017, 34(4): 364-369.

[23] 林万誉, 周思跃, 袁政鹏. 基于最小截取二乘法的点云数据去噪方法研究[J]. 计量与测试技术, 2016, 43(12): 60-62.

    Lin W Y, Zhou S Y, Yuan Z P. Research on the method for denoising of point cloud based on least trimmed squares[J]. Metrology & Measurement Technique, 2016, 43(12): 60-62.

[24] 赵凯, 徐友春, 李永乐, 等. 基于VG-DBSCAN算法的大场景散乱点云去噪[J]. 光学学报, 2018, 38(10): 1028001.

    Zhao K, Xu Y C, Li Y L, et al. Large-scale scattered point-cloud denoising based on VG-DBSCAN algorithm[J]. Acta Optica Sinica, 2018, 38(10): 1028001.

[25] 李明磊, 李广云, 宗文鹏. 激光扫描点云准确快速去噪方法[J]. 测绘通报, 2015( 12): 27- 29.

    Li ML, Li GY, Zong WP. Accurate and fast denoising method of laser-scanned point clouds[J]. Bulletin of Surveying and Mapping, 2015( 12): 27- 29.

[26] 李仁忠, 杨曼, 冉媛, 等. 基于方法库的点云去噪与精简算法[J]. 激光与光电子学进展, 2018, 55(1): 011008.

    Li R Z, Yang M, Ran Y, et al. Point cloud denoising and simplification algorithm based on method library[J]. Laser & Optoelectronics Progress, 2018, 55(1): 011008.

[27] 陈世超, 戴华阳, 王成, 等. 激光扫描数据的密集噪声剔除方法[J]. 激光与光电子学进展, 2019, 56(6): 062801.

    Chen S C, Dai H Y, Wang C, et al. Method for filtering dense noise from laser scanning data[J]. Laser & Optoelectronics Progress, 2019, 56(6): 062801.

[28] 赵宇明, 熊惠霖, 周越, 等. 模式识别[M]. 上海: 上海交通大学出版社, 2013.

    Zhao YM, Xiong HL, ZhouY, et al.Pattern recognition[M]. Shanghai: Shanghai Jiaotong University Press, 2013.

[29] 张瑜, 牟晓云. 统计学原理与应用[M]. 南京: 东南大学出版社, 2014.

    ZhangY, Mou XY. The principle and application of statistics[M]. Nanjing: Southeast University Press, 2014.

[30] 郭浩, 苏伟, 朱德海, 等. 点云库PCL从入门到精通[M]. 北京: 机械工业出版社, 2019.

    GuoH, SuW, Zhu DH, et al.Point cloud library PCL from entry to proficient[M]. Beijing: Chinese Machine Press, 2019.

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.

本文已被 2 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!