首页 > 论文 > 中国激光 > 46卷 > 4期(pp:404009--1)

融合改进场力和判定准则的点云特征规则化

Point Cloud Feature Regularization Based on Fusion of Improved Field Force and Judging Criterion

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

为了快速有效地获取散乱点云中的边界特征点和边界线, 提出了一种融合改进场力和判定准则的点云特征规则化算法。利用改进的k-d(k-dimensional)树搜索k邻域, 以采样点及其k邻域为参考点集拟合微切平面并向该平面投影,在微切平面上建立局部坐标系以将三维坐标转化成二维坐标, 利用场力和判定准则识别边界特征点; 依据矢量偏转角度和距离对边界特征点进行排序连接; 通过改进的三次B样条拟合算法对边界线进行平滑拟合。实验结果表明, 该算法能够快速有效地提取边界特征点, 且拟合后的边界线偏差量级为10-5 m, 具有较高的精度。

Abstract

In order to obtain the boundary feature points and boundary lines quickly and efficiently in the scattered point cloud, a point cloud feature regularization algorithm is proposed by means of the fusion of improved field force and judging criterion. An improved k-d (k-dimensional) tree method is first used to search the k neighbors of a sampling point. Then this sampling point and its k neighbors are used as the reference points to fit a micro-cut plane and project to this plane. The local coordinate system is established on the micro-cut plane and the three-dimensional coordinate is transformed into the two-dimensional coordinate. The boundary feature points are identified by use of field force and judging criterion. These boundary feature points are sorted and connected according to the vector deflection angle and distance. The boundary lines are smoothed by the improved cubic B-spline fitting algorithm. The experimental results show that the proposed algorithm can used to extract the boundary feature points quickly and efficiently, and the deviations of the fitted boundary lines are in the level of 10-5 m, indicating a relatively high precision.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:P232;O438

DOI:10.3788/cjl201946.0404009

所属栏目:测量与计量

基金项目:国家自然科学基金青年科学基金(41501502)、重庆市质量技术监督局科研计划(CQZJKY2018004)

收稿日期:2018-10-22

修改稿日期:2018-12-11

网络出版日期:2019-01-14

作者单位    点击查看

刘庆:武汉理工大学资源与环境工程学院, 湖北 武汉 430079
章光:武汉理工大学资源与环境工程学院, 湖北 武汉 430079
陈西江:武汉理工大学资源与环境工程学院, 湖北 武汉 430079

联系人作者:陈西江(cxj_0421@163.com)

【1】Wang Y H, Wang L J, Hao W,et al. A novel slicing-based regularization method for raw point clouds in visible IoT[J]. IEEE Access, 2018, 6: 18299-18309.

【2】Zhang L S, Cheng X J. Tunnel deformation analysis based on lidar points[J]. Chinese Journal of Lasers, 2018, 45(4): 0404004.
张立朔, 程效军. 基于激光点云的隧道形变分析方法[J]. 中国激光, 2018, 45(4): 0404004.

【3】Zhang T, Chen X J. Bridge amplitude monitoring with three-dimensional laser scanning technology[J]. Laser & Optoelectronics Progress, 2018, 55(5): 051409.
章涛, 陈西江. 利用三维激光扫描技术监测桥梁振幅[J]. 激光与光电子学进展, 2018, 55(5): 051409.

【4】Vrady T, Martin R R, Cox J. Reverse engineering of geometric models: an introduction[J]. Computer-Aided Design, 1997, 29(4): 255-268.

【5】Bazazian D, Casas J R, Ruiz-Hidalgo J. Fast and robust edge extraction in unorganized point clouds[C]∥2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2015: 1-8.

【6】Hackel T, Wegner J D, Schindler K. Contour detection in unstructured 3D point clouds[C]∥IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 1610-1618.

【7】Xi X H, Wan Y P, Wang C. Building boundaries extraction from points cloud using an imageedge detection method[C]∥IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016: 1270-1273.

【8】Zhang Y H, Geng G H, Wei X R, et al. Feature extraction of point clouds using the DBSCAN clustering[J]. Journal of Xidian University (Natural Science), 2017, 44(2): 114-120.
张雨禾, 耿国华, 魏潇然, 等. 采用密度空间聚类的散乱点云特征提取方法[J]. 西安电子科技大学学报(自然科学版), 2017, 44(2): 114-120.

【9】Dong W. Feature extraction of the building point cloud by using geometrical characteristics of adjacent points[J]. Laser & Optoelectronics Progress, 2018, 55(7): 071006.
董伟. 利用邻近点几何特征实现建筑物点云特征提取[J]. 激光与光电子学进展, 2018, 55(7): 071006.

【10】Chen P, Tan Y W, Li L. Extraction of building′s feature lines based on 3-D terrestrial laser scanning[J]. Laser Journal, 2016, 37(3): 9-11.
陈朋, 谭晔汶, 李亮. 地面三维激光扫描建筑物点云特征线提取[J]. 激光杂志, 2016, 37(3): 9-11.

【11】Chen Y R, Wang Y B, Peng Z J, et al. Improved algorithm for extraction of boundary characteristic point from scattered point cloud[J]. Computer Engineering and Applications, 2012, 48(23): 177-180, 190.
陈义仁, 王一宾, 彭张节, 等. 一种改进的散乱点云边界特征点提取算法[J]. 计算机工程与应用, 2012, 48(23): 177-180, 190.

【12】Sun D Z, Fan Z X, Li Y R. Automatic extraction of boundary characteristic from scatter data[J]. Journal of Huazhong University of Science and Technology (Nature Science), 2008, 36(8): 82-84.
孙殿柱, 范志先, 李延瑞. 散乱数据点云边界特征自动提取算法[J]. 华中科技大学学报(自然科学版), 2008, 36(8): 82-84.

【13】Sun J H. Research on key technologies of point cloud segmentation and fusion[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2013: 9-24.
孙金虎. 点云模型分割与融合关键技术研究[D]. 南京: 南京航空航天大学, 2013: 9-24.

【14】Ma L M, Xu Y, Li Z X. Fast k-nearest neighbors searching algorithm for scattered points based on dynamic grid decomposition[J]. Computer Engineering, 2008, 34(8): 10-11, 21.
马骊溟, 徐毅, 李泽湘. 基于动态网格划分的散乱点k邻近快速搜索算法[J]. 计算机工程, 2008, 34(8): 10-11, 21.

【15】Zhang R. An improved spatial grid K-neighborhood search algorithm[J]. Technology Innovation and Application, 2017(29): 5-6.
张蓉. 一种改进的立体栅格K邻域搜索算法[J]. 科技创新与应用, 2017(29): 5-6.

【16】Gu Y Y, Jiang X F, Zhang L. The boundary extraction of point cloud with hole in surface reconstruction[J]. Journal of Suzhou University (Engineering Science Edition), 2008, 28(2): 56-61.
顾园园, 姜晓峰, 张量. 曲面重构中带孔洞点云数据的边界提取算法[J]. 苏州大学学报(工科版), 2008, 28(2): 56-61.

【17】Shi F Z. Computer aided geometric design and non-uniform rational B-spline[M]. 2nd ed. Beijing: Higher Education Press, 2013: 133-149.
施法中. 计算机辅助几何设计与非均匀有理B样条[M]. 2版. 北京: 高等教育出版社, 2013: 133-149.

【18】Zeng Z, Xieeryazidan A, Shen C P. A research on curve fitting of cubic B-spline wavelets[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(6): 891-895.
曾卓, 阿达依·谢尔亚孜旦, 申传鹏. 三次B样条小波的曲线拟合研究[J]. 机械科学与技术, 2018, 37(6): 891-895.

【19】Duan Z Y, Wang N, Yang X, et al. An algorithm of improved B-spline curve fitting[J]. Machinery Design & Manufacture, 2016(5): 17-19, 23.
段振云, 王宁, 杨旭, 等. 一种改进B样条曲线拟合算法研究[J]. 机械设计与制造, 2016(5): 17-19, 23.

引用该论文

Liu Qing,Zhang Guang,Chen Xijiang. Point Cloud Feature Regularization Based on Fusion of Improved Field Force and Judging Criterion[J]. Chinese Journal of Lasers, 2019, 46(4): 0404009

刘庆,章光,陈西江. 融合改进场力和判定准则的点云特征规则化[J]. 中国激光, 2019, 46(4): 0404009

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF