簡易檢索 / 詳目顯示

研究生: 陳欣瑜
Hsin-Yu Chen
論文名稱: 無控制點的雷射掃描之隧道點雲套合研究
Research on Point Cloud Registration of Tunnels Using Laser Scanning without Control Points
指導教授: 謝佑明
Yo-Ming Hsieh
口試委員: 陳鴻銘
Hung-Ming Chen
莊子毅
Tzu-Yi Chuang
學位類別: 碩士
Master
系所名稱: 工程學院 - 營建工程系
Department of Civil and Construction Engineering
論文出版年: 2023
畢業學年度: 112
語文別: 中文
論文頁數: 84
中文關鍵詞: 雷射掃描點雲套合隧道掃描控制點
外文關鍵詞: Laser Scanning, Point Cloud Registration, Tunnel Scanning, Control Points
相關次數: 點閱:88下載:18
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 在山巒連綿不斷的台灣,為方便互通往來,隧道是不可或缺的基礎建設,其穩固與否與人民安全息息相關,隧道的定期檢測時,隧道襯砌的狀況必須被評估,以維護其安全性與功能性,而雷射掃描取得隧道點雲是得以快速探討完整襯砌變形之有效方法。
    由於單次雷射掃描無法覆蓋整個隧道場景,必須進行多次分段地掃描並將不同測站掃描之點雲套合至同坐標系,從而得出完整的隧道點雲以進行後續的分析應用。傳統隧道雷射掃描會在隧道中佈設共軛球或控制點,以方便點雲的套合。然而,佈設控制點是耗工費時的任務。
    本研究經文獻回顧與實作評估多個套合方法,以期能在無控制點的情況下完成隧道雷射掃描之點雲套合。透過模擬點雲資料對套合方法進行測試及評估,挑選出最適合用於隧道點雲套合的方法,並分析不同的隧道場景和參數對點雲套合的影響。在本研究中,我們成功省去尋找控制點步驟,實現無控制點的點雲套合。


    In the continuous mountainous terrain of Taiwan, tunnels are indispensable infrastructure for facilitating mutual transportation. Their stability is closely tied to public safety. During regular inspections, the condition of tunnel linings must be assessed to maintain their safety and functionality. Laser scanning has proven to be a rapid and effective method for acquiring tunnel point clouds, enabling a comprehensive exploration of lining deformations..
    Since a single laser scan is unable to cover the entire tunnel scene, it is necessary to perform multiple segmented scans. The point clouds obtained from different stations must then be registered to the same coordinate system to generate a complete tunnel point cloud for subsequent analytical applications. Traditional tunnel laser scanning involves deploying conjugate spheres or control points within the tunnel to facilitate point cloud registration. However, the placement of control points is a labor-intensive and time-consuming task.
    This study conducts a literature review and practical evaluations of various registration methods, aiming to achieve point cloud registration in tunnel laser scanning without the need for control points. Through simulations using point cloud data, different registration methods are tested and assessed to identify the most suitable approach for tunnel point cloud registration. The research also analyzes the impact of different tunnel scenarios and parameters on point cloud registration. In this study, we successfully eliminate the step of searching for control points, achieving point cloud registration without the use of control points.

    論文摘要 I ABSTRACT II 致謝 IV 目錄 V 圖目錄 VIII 表目錄 XII 緒論 1 1.1 研究動機與目的 1 1.2 研究方法 1 1.3 論文架構 4 第二章 文獻回顧 7 2.1 點雲套合方法 7 2.1.1 粗套合 7 2.1.2 細套合 8 2.1.3 多點雲套合 9 2.2 非線性最小平方問題最佳化方法 11 2.3點雲於隧道變形偵測的應用 12 2.4 無控制點的點雲套合 13 第三章 研究工具與方法 15 3.1硬體設備 15 3.2 開放原始碼程式庫及軟體 16 3.2.1 Eigen 17 3.2.2 PCL 17 3.2.3 Open3D 17 3.2.4 CloudCompare 18 3.2.5 SketchUp 18 第四章 選定最佳套合方法 19 4.1 生成模擬點雲 19 4.2點雲套合方法初選 26 4.3建立套合方法評判機制 26 4.3.1不同視角點雲 28 4.3.2不同視角點雲 30 4.3.1 不同視角點雲套合結果 32 4.3.2 前後段點雲套合結果 38 4.4選定最佳套合方法 46 4.5小結 49 第五章 參數研究與場景測試 51 5.1 前後段隧道重疊距離影響 51 5.2 降採樣影響 52 5.2.1不同視角點雲 52 5.2.2前後段點雲 54 5.3誤差標準差影響 56 5.3.1不同視角點雲 57 5.3.2前後段點雲 61 5.4 彎曲隧道模擬點雲套合 63 5.5小結 69 第六章 真實點雲資料套合 71 6.1舊百吉隧道套合 71 6.1.1案例介紹 71 6.1.2套合結果與討論 72 6.2舊五堵隧道套合 75 6.2.1案例介紹 75 6.2.2套合結果與討論 76 6.3小結 78 第七章 結論與建議 79 7.1 結論 79 7.2 建議與未來展望 80 參考文獻 81

    [1] 張晉睿, 以雷射掃描自動化鋼結構虛擬組立之初探, in 營建工程系. 2023, 國立臺灣科技大學: 台北市. p. 100.
    [2] 陳昭旭, 低成本LiDAR用於隧道3D雷射掃描初探, in 營建工程系. 2021, 國立臺灣科技大學: 台北市. p. 110.
    [3] Cheng, L., et al., Registration of laser scanning point clouds: A review. Sensors, 2018. 18(5): p. 1641.
    [4] Fischler, M.A. and R.C. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 1981. 24(6): p. 381-395.
    [5] Al-Durgham, K., A. Habib, and E. Kwak, RANSAC approach for automated registration of terrestrial laser scans using linear features. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013. 2: p. 13-18.
    [6] Biber, P. and W. Straßer. The normal distributions transform: A new approach to laser scan matching. in Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003)(Cat. No. 03CH37453). 2003. IEEE.
    [7] Theiler, P.W., J.D. Wegner, and K. Schindler, Markerless point cloud registration with keypoint-based 4-points congruent sets. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013. 2: p. 283-288.
    [8] Theiler, P.W., J.D. Wegner, and K. Schindler, Fast registration of laser scans with 4-points congruent sets-what works and what doesn't. ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, 2014. 2: p. 149-156.
    [9] Zhou, Q.-Y., J. Park, and V. Koltun. Fast global registration. in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14. 2016. Springer.
    [10] Besl, P.J. and N.D. McKay. Method for registration of 3-D shapes. in Sensor fusion IV: control paradigms and data structures. 1992. Spie.
    [11] Chen, Y. and G. Medioni, Object modelling by registration of multiple range images. Image and vision computing, 1992. 10(3): p. 145-155.
    [12] Myronenko, A. and X. Song, Point set registration: Coherent point drift. IEEE transactions on pattern analysis and machine intelligence, 2010. 32(12): p. 2262-2275.
    [13] Pan, Y., et al. Iterative global similarity points: A robust coarse-to-fine integration solution for pairwise 3d point cloud registration. in 2018 International Conference on 3D Vision (3DV). 2018. IEEE.
    [14] Kuhn, H.W., The Hungarian method for the assignment problem. Naval research logistics quarterly, 1955. 2(1‐2): p. 83-97.
    [15] Liang, X., et al., Image-guided registration of unordered terrestrial laser scanning point clouds for urban scenes. IEEE Trans. Geosci. Remote, 2012. 50: p. 661-670.
    [16] Choi, S., Q.-Y. Zhou, and V. Koltun. Robust reconstruction of indoor scenes. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
    [17] Carlone, L., et al., Planar pose graph optimization: Duality, optimal solutions, and verification. IEEE Transactions on Robotics, 2016. 32(3): p. 545-565.
    [18] Lu, F. and E. Milios, Globally consistent range scan alignment for environment mapping. Autonomous robots, 1997. 4: p. 333-349.
    [19] Borrmann, D., et al. The efficient extension of globally consistent scan matching to 6 DOF. in 4th International Symposium on 3D Data Processing, Visualization and Transmission, 3DPVT 2008-Proceedings. 2008. Georgia Institute of Technology.
    [20] Open3D. Multiway registration. Available from: http://www.open3d.org/docs/release/tutorial/pipelines/multiway_registration.html.
    [21] Open3D. GlobalOptimization.cpp. Available from: https://github.com/islorg/Open3D/blob/master/cpp/open3d/pipelines/registration/GlobalOptimization.cpp.
    [22] Björck, Å., Numerical methods for least squares problems. 1996: SIAM.
    [23] Gavin, H.P., The Levenberg-Marquardt algorithm for nonlinear least squares curve-fitting problems. Department of civil and environmental engineering, Duke University, 2019. 19.
    [24] Moré, J.J. The Levenberg-Marquardt algorithm: implementation and theory. in Numerical analysis: proceedings of the biennial Conference held at Dundee, June 28–July 1, 1977. 2006. Springer.
    [25] Wang, W., et al., Applications of terrestrial laser scanning for tunnels: a review. Journal of Traffic and Transportation Engineering (English Edition), 2014. 1(5): p. 325-337.
    [26] Lindenbergh, R., N. Pfeifer, and T. Rabbani. Accuracy analysis of the Leica HDS3000 and feasibility of tunnel deformation monitoring. in Proceedings of the ISPRS Workshop, Laser scanning. 2005.
    [27] Van Gosliga, R., R. Lindenbergh, and N. Pfeifer, Deformation analysis of a bored tunnel by means of terrestrial laser scanning. 2006: Citeseer.
    [28] Lindenbergh, R., et al., Structural monitoring of tunnels using terrestrial laser scanning. Reports on geodesy, 2009: p. 231-238.
    [29] Han, J.-Y., J. Guo, and Y.-S. Jiang, Monitoring tunnel deformations by means of multi-epoch dispersed 3D LiDAR point clouds: An improved approach. Tunnelling and Underground Space Technology, 2013. 38: p. 385-389.
    [30] Han, J.-Y., J. Guo, and Y.-S. Jiang, Monitoring tunnel profile by means of multi-epoch dispersed 3-D LiDAR point clouds. Tunnelling and underground space technology, 2013. 33: p. 186-192.
    [31] Zhang, J., et al., Natural forest ALS-TLS point cloud data registration without control points. Journal of Forestry Research, 2023. 34(3): p. 809-820.
    [32] Bash, E.A., et al., A Multi-Resolution Approach to Point Cloud Registration without Control Points. Remote Sensing, 2023. 15(4): p. 1161.
    [33] Livox. Livox Mid-70 User Manual. 2023; Available from: https://www.livoxtech.com/mid-70.
    [34] Livox. Livox Horizon User Manual. 2023; Available from: https://www.livoxtech.com/horizon.
    [35] Eigen (C++ library) - Wikipedia. Available from: https://en.wikipedia.org/wiki/Eigen_(C%2B%2B_library).
    [36] Point Cloud Library - Wikipedia. Available from: https://en.wikipedia.org/wiki/Point_Cloud_Library.
    [37] Zhou, Q.-Y., J. Park, and V. Koltun, Open3D: A modern library for 3D data processing. arXiv preprint arXiv:1801.09847, 2018.
    [38] CloudCompare. Available from: https://www.danielgm.net/cc/.
    [39] Girardeau-Montaut, D., CloudCompare. France: EDF R&D Telecom ParisTech, 2016. 11: p. 5.
    [40] CloudCompare - Wikipedia. Available from: https://en.wikipedia.org/wiki/CloudCompare.
    [41] SketchUp - Wikipedia. Available from: https://en.wikipedia.org/wiki/SketchUp.
    [42] Dorigo, M., M. Birattari, and T. Stutzle, Ant colony optimization. IEEE computational intelligence magazine, 2006. 1(4): p. 28-39.
    [43] 都市基礎工程組, 內. 市區道路及附屬工程設計規範(111 02修正版). 2022; Available from: https://myway.cpami.gov.tw/wiki/wikiSession/1035?fbclid=IwAR2xtpJlsS81T6jqRq8DzkNHCkM3ysod9ppmowAYX4PUgD153xqVDlIuZ-s.
    [44] Rusu, R.B., N. Blodow, and M. Beetz. Fast point feature histograms (FPFH) for 3D registration. in 2009 IEEE international conference on robotics and automation. 2009. IEEE.
    [45] (PCL), P.C.L. pcl::registration::LUM< PointT > Class Template Reference. Available from: https://pointclouds.org/documentation/classpcl_1_1registration_1_1_l_u_m.html.
    [46] Coherent Point Drift code. Available from: https://github.com/weigert/CoherentPointDrift.
    [47] Dong, Z., et al., A novel binary shape context for 3D local surface description. ISPRS Journal of Photogrammetry and Remote Sensing, 2017. 130: p. 431-452.
    [48] Open3D. Voxelization. Available from: https://www.open3d.org/docs/latest/tutorial/Advanced/voxelization.html.
    [49] 桃園觀光導覽網. 舊百吉隧道. Available from: https://travel.tycg.gov.tw/zh-tw/travel/attraction/1177.
    [50] 新北市觀光旅遊網. 五堵隧道自行車道. Available from: https://newtaipei.travel/zh-tw/attractions/detail/403018.

    QR CODE