臺灣博碩士論文加值系統

預裝在鋼結構中扮演著重要角色，它指的是在現場施工之前，將結構元件預先在製造廠或預裝場進行組裝。然而，當前預裝過程需要耗費大量人力和時間，以確保元件在合理誤差範圍內成功組裝。本研究開發了光達模擬器作為研究工具，以此模擬實務上以光達(LiDAR，Light Detection and Ranging) 取得構件的掃描資料。

藉由掃描鋼構件所獲得的點雲資料，本研究提出了基於點雲資料的自動化螺栓孔辨識演算法，該演算法能夠高度自動化地處理螺栓孔辨識問題。透過這項自動化辨識方法，本研究在多個案例中成功辨識螺栓孔平均比例高達99.8%，顯示了演算法的有效性與準確性。

最後，本研究提出利用構件的特徵點與幾何條件搭配窮舉法尋找兩構件匹配的轉換矩陣，透過計算成功得到兩個構件的接合姿態。此方法使得在電腦中進行虛擬組裝成為可能，並可省略實務上對構件進行預裝的流程。這項虛擬組裝技術在此階段帶來更高的安全性與效率，並有望在鋼結構領域中發揮重要的應用價值。

Pre-assembly plays a significant role in steel structures, referring to the process of preassembling structural components in manufacturing plants or pre-assembly sites before on-site construction. However, pre-assembly may lead to work safety accidents due to various reasons. To address this issue, this study developed a LiDAR simulator as a research tool with highly flexible and versatile parameter settings, capable of generating highly realistic scan data according to user requirements.
Upon obtaining scan data of steel components through the LiDAR simulator, this study proposed an automated bolt hole recognition algorithm based on point cloud data. The algorithm efficiently handles complex bolt hole recognition problems and successfully restores unidentified bolt holes in the components. Through this automated recognition method, the study achieved an average success rate of 99.8% in recognizing and repairing bolt holes in multiple cases, demonstrating the effectiveness and accuracy of the algorithm.
Finally, the study presented a method using an exhaustive search to find the transformation matrix for matching two components, enabling successful calculation of their joint configuration. This method allows virtual assembly in a computer environment, eliminating the need for practical pre-assembly procedures. The virtual assembly technique contributes to higher safety and efficiency at this stage, showing promising potential for significant application in the field of steel structures.

論文摘要 I
ABSTRACT II
目錄 IV
圖目錄 VII
表目錄 XI
第一章 緒論 1
1.1 研究動機與目的 1
1.2 研究流程 2
1.2.1 論文架構 3
第二章 文獻回顧 5
2.1 虛擬組裝 5
2.2 影響光達資料的強度因素 6
2.3 點雲資料中辨識邊界點 7
第三章 研究工具與演算法 8
3.1 研究工具 8
3.1.1 AutoCAD 8
3.1.2 Revit & Revit API 9
3.1.3 Visual Studio 2019 9
3.1.4 VTK 9
3.1.5 OpenMP 10
3.1.6 PCL 10
3.1.7 Eigen 10
3.1.8 CloudCompare 11
3.2 研究用演算法 11
3.2.1 主成分分析 11
3.2.2 聚類分析 12
3.2.3 RANSAC 12
3.2.4 Octree 13
3.2.5 奇異值分解與旋轉矩陣 13
第四章 光達模擬器 15
4.1 光達模擬器運作流程 15
4.2 光達模擬器設定與輸入 16
4.3 光達模擬器雷射掃描 21
4.3.1 光達模擬器掃描流程 21
4.3.2 光達模擬器掃描範圍 24
4.3.3 利用平行計算提升掃描效率 28
4.4 雷射距離與入射角對掃描強度值探討 30
4.5 光達模擬器其他功能 34
4.5.1 掃描完整度之評估方式 35
4.5.2 不同掃描之重疊率 37
4.6 成果展示 37
4.7 小結 39
第五章 自動化螺栓孔辨識 40
5.1 自動化螺栓孔辨識處理流程 40
5.2 點雲降採樣 42
5.3 第一階段螺栓孔擬合 44
5.4 第二階段螺栓孔擬合 48
5.5 螺栓孔數量檢查與修復 53
5.6 無法修復的案例 57
5.7 螺栓孔辨識之成果展示 61
5.8 小結 64
第六章 自動化虛擬組裝 65
6.1 自動化虛擬組裝流程 65
6.2 特徵點找尋 66
6.3 特徵點匹配 69
6.4 成果展示 73
6.5 小結 80
第七章 結論與建議 81
7.1 結論 81
7.2 建議與未來展望 82
第8章 參考文獻 84

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