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基于激光强度分类的机载与地面激光雷达点云配准方法

Registration Method for Airborne and Terrestrial Light Detection and Ranging Point Cloud Based on Laser Intensity Classification

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摘要

目前, 机载与地面激光雷达(LiDAR)点云配准方法大多利用三维点云的几何信息来获取机载与地面LiDAR点云的同名特征, 并计算点云坐标转换参数, 实现点云配准;提出了一种基于激光强度分类的配准新方法, 首先对机载与地面LiDAR点云的激光强度信息进行纠正与分类, 然后基于分类结果提取特征平面, 将特征平面间的拓扑关系与分类结果作为约束条件, 匹配得到同名特征平面, 最后计算坐标转换参数, 实现机载与地面LiDAR点云配准。实验结果表明:与传统方法相比, 所提方法可以减小机载与地面LiDAR因扫描角度、点密度不同而导致的配准误差;在机载与地面LiDAR同名特征几何形状不完全一致的情况下, 所提方法仍可得到较好的配准效果。

Abstract

Numerous registration methods for airborne and terrestrial light detection and ranging (LiDAR) point cloud utilize geometry information of three-dimensional point cloud. Corresponding features of airborne and terrestrial LiDAR point cloud are matched, and point cloud coordinate transformation parameters are calculated to realize point cloud registration. A new registration method based on laser intensity classification is proposed. Firstly, the laser intensity of airborne and terrestrial LiDAR point cloud is corrected and classified. Then, the plane features are extracted by the classification results. The corresponding plane features are matched taking topological relationship and the classification results as constraint conditions. Finally, the coordinate transformation parameters are calculated to register the airborne and terrestrial LiDAR point cloud. The results show that compared with traditional methods, the proposed method can reduce registration errors from differences of the scanning angle and density between airborne and terrestrial LiDAR. The proposed method can still achieve accurate registration effect when the geometry shapes of the corresponding features of airborne and terrestrial LiDAR are not completely identical.

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中图分类号:P237

DOI:10.3788/lop55.062803

所属栏目:遥感与传感器

基金项目:国家自然科学基金(41671449)

收稿日期:2017-11-23

修改稿日期:2017-12-31

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作者单位    点击查看

郭王:同济大学测绘与地理信息学院, 上海 200092
程效军:同济大学测绘与地理信息学院, 上海 200092

联系人作者:郭王(1983guowang@tongji.edu.cn)

备注:郭王(1983-), 男, 博士研究生, 主要从事LiDAR数据处理与多源LiDAR融合方面的研究。E-mail: 1983guowang@tongji.edu.cn

【1】Besl P J, McKay N D. A method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2): 239-256.

【2】Rusinkiewicz S, Levoy M. Efficient variants of the ICP algorithm[C]∥Proceedings of 3rd International Conference on 3D Digital Imaging and Modeling, 2001: 145-152.

【3】Gelfand N, Mitra N J, Guibas L J,et al. Robust global registration[C]∥Proceedings of Eurographics on Geometry Processing, 2005, 2(3): 197-206.

【4】Au O K C, Tai C L, Cohen-Or D, et al. Electors voting for fast automatic shape correspondence[C]∥Proceedings of Eurographics on Computer Graphics Forum, 2010, 29(2): 645-654.

【5】Aiger D, Mitra N J, Cohen-Or D. 4-points congruent sets for robust pairwise surfaceregistration[J]. ACM Transactions on Graphics, 2008, 27(3): 85.

【6】Chang W, Zwicker M. Automatic registration for articulated shapes[C]∥Proceedings of the Symposium on Geometry Processing, 2008, 27(5): 1459-1468.

【7】Von Hansen W, GrossH, Thoennessen U. Line-based registration of terrestrial and airborne LIDAR data[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2008, 37: 161-166.

【8】Holz D, Behnke S. Registration ofnon-uniform density 3D point clouds using approximate surface reconstruction[C]∥Proceedings of Joint 45th International Symposium on Robotics and 8th German Conference on Robotics, 2014: 1-7.

【9】Cheng L, Tong L H, Li M C, et al. Semi-automatic registration of airborne and terrestrial laser scanning data using building corner matching with boundaries as reliability check[J]. Remote Sensing, 2013, 5(12): 6260-6283.

【10】Yang B S, Zang Y F, Dong Z, et al. An automated method to register airborne and terrestrial laser scanning point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 109: 62-76.

【11】Fang W, Huang X F, Zhang F, et al. Mural image rectification based on correction of laser point cloud intensity[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(5): 541-547.
方伟, 黄先锋, 张帆, 等. 依据点云强度校正的壁画纠正[J]. 测绘学报, 2015, 44(5): 541-547.

【12】Anttila K, Hakala T, Kaasalainen S, et al. Calibrating laser scanner data from snow surfaces: Correction of intensity effects[J]. Cold Regions Science and Technology, 2016, 121: 52-59.

【13】Tan K, Cheng X J. Intensity data correction based on incidence angle and distance for terrestrial laser scanner[J]. Journal of Applied Remote Sensing, 2015, 9(1): 094094.

【14】Cheng X L, Cheng X J, Li Q, et al. Laser intensity correction of terrestrial 3D laser scanning based on sectional polynomial model[J]. Laser & Optoelectronics Progress, 2017, 54(11): 112802.
程小龙, 程效军, 李泉, 等. 基于分段多项式模型的地面三维激光扫描激光强度改正[J]. 激光与光电子学进展, 2017, 54(11): 112802.

【15】Cheng X J, Guo W, Li Q, et al. Joint classification method for terrestrial LiDAR point cloud based on intensity and color information[J]. Chinese Journal of Lasers, 2017, 44(10): 1010007
程效军, 郭王, 李泉, 等. 基于强度与颜色信息的地面LiDAR点云联合分类方法[J]. 中国激光, 2017, 44(10): 1010007.

【16】Cheng X J, Cheng X L, Hu M J, et al. Buildings detection and contour extraction by the fusion of aerial images and LIDAR point cloud[J]. Chinese Journal of Lasers, 2016, 43(5): 0514002.
程效军, 程小龙, 胡敏捷, 等. 融合航空影像和LIDAR点云的建筑物探测及轮廓提取[J]. 中国激光, 2016, 43(5): 0514002.

【17】Cordella L P, Foggia P, Sansone C, et al. An efficient algorithm for the inexact matching of ARG graphs using a contextual transformational model[C]∥Proceedings of the 13th International Conference on Pattern Recognition, 1996, 3: 180-184.

引用该论文

Guo Wang,Cheng Xiaojun. Registration Method for Airborne and Terrestrial Light Detection and Ranging Point Cloud Based on Laser Intensity Classification[J]. Laser & Optoelectronics Progress, 2018, 55(6): 062803

郭王,程效军. 基于激光强度分类的机载与地面激光雷达点云配准方法[J]. 激光与光电子学进展, 2018, 55(6): 062803

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