基于面组合投票的地面站点云配准方法
A Registration Method for TLS Based on Patches Combination and Voting
DOI: 10.12677/GST.2017.53013, PDF, HTML, XML, 下载: 1,414  浏览: 2,725  国家科技经费支持
作者: 周桐, 梁福逊, 杨必胜：武汉大学测绘遥感信息工程国家重点实验室，湖北 武汉
关键词: 激光扫描；配准；面片；组合；投票；Laser Scanning； Registration； Patches； Combination； Voting
摘要: 地面站三维激光扫描在工程测量，三维重建，文物保护等领域发挥越来越重要的作用。地面激光扫描仪由于视角和遮挡的问题，必须通过多站采集拼接的方式获取数据，故配准是必不可少的步骤。文章提出了一种自动化激光点云配准方案，从点云中提取面片，通过面组合和最佳投票的方法计算转换参数，并自动完成多站点云的坐标转换与输出。经实验证实，本文的配准方法能够稳健且高效地完成城市环境点云的配准，有很好的实用价值。
Abstract: Terrain Laser Scanning (TLS) plays an important role in engineering survey, 3D reconstruction and cultural relics preservation. The way laser scanner acquiring point cloud data must be several stations attaching because of views interruption, therefore, registration of point cloud is necessary. This article put forward an automatic registration method for TLS, extracts patches in point cloud firstly, counting transform parameters using patches combination and voting, and output the transformed point cloud automatically in the end. Experiments shows that our method can finish the registration of multiple-station city environment TLS robustly and efficiently, which makes the method useful in practice.
文章引用：周桐, 梁福逊, 杨必胜. 基于面组合投票的地面站点云配准方法[J]. 测绘科学技术, 2017, 5(3): 106-118. https://doi.org/10.12677/GST.2017.53013

参考文献

[1] 杨现辉. 基于对偶四元数的点云配准算法研究[D]: [硕士学位论文]. 南京: 南京航空航天大学, 2010.
[2] 张步. 三维激光点云数据配准研究[D]: [硕士学位论文]. 西安: 西安科技大学, 2015.
[3] 张梅, 文静华, 张祖勋, 等. 基于欧氏距离测度的激光点云配准[J]. 测绘科学, 2010, 35(3): 5-8.
[4] 吴耀. 基于SIFT算子的地面激光点云数据配准方法研究[D]: [硕士学位论文]. 抚州: 东华理工大学, 2013.
[5] 钱鹏鹏, 郑德华, 李佳龙, 等. 一种基于SIFT算子的深度图像自动配准方法[J]. 勘察科学技术, 2014(3): 11-14.
[6] Aiger, D., Mitra, N.J. and Cohen, D. (2008) 4-Points Congruent Sets for Robust Pairwise Surface Registration. ACM Siggraph, 27, 85.
https://doi.org/10.1145/1360612.1360684
[7] Theiler, P.W., Wegner, J.D. and Schindler, K. (2013) Marker-Less Point Cloud Registration with Keypoint-Based 4-Points Congruent Sets. Remote Sensing and Spatial Information Sciences, 5, 283-288.
https://doi.org/10.5194/isprsannals-ii-5-w2-283-2013
[8] Theiler, P.W., Wegner, J.D. and Schindler, K. (2014) Keypoint-Based 4-Points Congruent Sets—Automated Marker-Less Registration of Laser Scans. Journal of Photogrammetry & Remote Sensing, 96, 149-163.
https://doi.org/10.1016/j.isprsjprs.2014.06.015
[9] Li, N., Cheng, P., Sutton, M.A., et al. (2005) Three-Dimensional Point Cloud Registration by Matching Surface Features with Relaxation Labeling Method. Experimental Mechanics, 45, 71-82.
https://doi.org/10.1007/BF02428992
[10] 彭博. 激光三维扫描点云数据的配准研究[D]: [硕士学位论文]. 天津: 天津大学, 2011.
[11] Yang, B., Dong, Z., Liang, F., et al. (2016) Automatic Registration of Large-Scale Urban Scene Point Clouds Based on Semantic Feature Points. Journal of Photogrammetry & Remote Sensing, 113, 43-58.
https://doi.org/10.1016/j.isprsjprs.2015.12.005
[12] Yang, B. and Zang, Y. (2014) Automated Registration of Dense Terrestrial Laser-Scanning Point Clouds Using Curves. Journal of Photogrammetry & Remote Sensing, 95, 109-121.
https://doi.org/10.1016/j.isprsjprs.2014.05.012
[13] Yang, B. and Zang, Y. (2014) Automated Registration of Dense Terrestrial Laser-Scanning Point Clouds Using Curves. Journal of Photogrammetry & Remote Sensing, 95, 109-121.
https://doi.org/10.1016/j.isprsjprs.2014.05.012
[14] 郑敏辉, 臧玉府, 梁福逊, 等. 不同场景的地面激光点云配准方法研究[J]. 测绘通报, 2015(8): 30-34.

为你推荐