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研究生： 陳昭旭
Chao-Hsu Chen
論文名稱： 低成本LiDAR用於隧道3D雷射掃描初探
A Preliminary Study of Applying Low-Cost LiDAR to Tunnel 3D Laser Scanning
指導教授： 謝佑明
Yo-Ming Hsieh
口試委員: 陳鴻銘
Hung-Ming Chen
王泰典
Tai-Tien Wang
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： 2021
畢業學年度： 109
語文別： 中文
論文頁數： 110
中文關鍵詞： 隧道掃描 、光達 、粒子群最佳化 、點雲 、Livox
外文關鍵詞： Tunnel Scanning, LiDAR, Livox, Point Cloud, PSO
相關次數： 點閱：263  下載：12 
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    • 現今雷射掃描隧道檢測經常使用全站式雷射掃描儀器，其精度高但要價不菲且耗時費力，測量人員進行檢測時往往需要耗費極大精神與體力，難以省力並有效率地進行隧道定期檢測。
    本研究模擬並評估相較於全站式雷射掃描儀價位等成本較低的無人駕駛車輛用LiDAR感測器用於隧道檢測雷射掃描項目之可行性，經文獻回顧與實際測試之評估後選擇來自Livox兩款精確度為2公分的低成本LiDAR感測器，Livox Mid-70及Livox Horizon，模擬其非重複式之掃描模式及用於半圓形及三心拱斷面隧道之掃描，並以粒子群最佳化演算法自動化配置在不同數量組合之Mid-70及Horizon下各感測器的最佳姿態，比較並評估一次所能達到的最長完整掃描距離、一固定長度隧道段內單位點雲密度等點雲分布狀況及該數量組合花費之單位價錢及重量成本，以得出一最佳數量組合及其姿態，即為Livox LiDAR用於隧道檢測雷射掃描之最佳配置。
    本研究將最佳化之結果，兩Livox Mid-70及兩Livox Horizon的組合及其姿態以一平台搭載並於多個隧道場景進行實際測試，檢視該模擬配置於實際場景之點雲分布狀況。結果初步顯示本研究之最佳配置應能以低成本完整掃描隧道表面，然對於較精準的結果及自動化掃描仍有較大提升空間。

    Nowadays, total-station laser scanning instruments are used to perform tunnel inspections. Although with high accuracy, they are expensive and take lots of time when using them, which consumes both physical and mental energy and makes people exhausted when doing tunnel inspections, making they difficult to perform periodical tunnel inspections effortlessly and efficiently.
    In this article, we simulate the LiDAR sensors for driverless vehicles with lower cost comparing to total-station laser scanning instruments and evaluate the feasibility of using them in tunnel inspection. After evaluation by literature review and actual test, two low-cost LiDAR sensors with accuracy of 2 cm from Livox, Livox Mid-70 and Livox Horizon, are selected.
    We simulate their non-repetitive scanning pattern and point cloud distribution while scanning semi-circular tunnels and three-center arch tunnels. Then the particle swarm optimization algorithm is used to automatically configure the optimized poses of the LiDAR sensors under different combinations of Mid-70 and Horizon to compare the longest completed scanning distance that can be achieved at one time, the unit point cloud density in a fixed length tunnel section, and the unit price and weight cost. Thus, we can obtain the optimized quantity combination and the poses of LiDAR sensors as the best configuration of Livox LiDAR for tunnel scanning.
    Finally, the optimized result, the combination of two Livox Mid-70 and two Livox Horizon and their poses, are carried by a platform for actual testing in several tunnel scenes to check the point cloud distribution of the simulation configuration in actual scenes. Preliminary results suggest the optimized configuration can scan tunnel surface completely at low cost, but further enhancements are necessary for more precise results and automatically scanning.

    論文摘要 I ABSTRACT II 目錄 IV 圖目錄 VIII 表目錄 XIV 第1章 緒論 1 1.1 研究動機與目的 1 1.2 論文架構 3 第2章 文獻回顧 5 2.1 車用LiDAR感測器之精確度 5 2.2 車用LiDAR於隧道檢測之應用 6 2.3 粒子群最佳化(Particle Swarm Optimization) 9 2.4 視覺慣性里程計(Visual-Inertial Odometry) 11 2.5 點雲套合(Point Cloud Registration) 14 第3章 研究工具與方法 17 3.1 研究方法 17 3.1.1 LiDAR感測器之比較與選用 18 3.1.2 掃描模式之模擬 18 3.1.3 使用最佳化演算法自動化配置感測器姿態 19 3.1.4 點雲資料收集及後處理 19 3.1.5 實際測試 19 3.2 硬體設備 19 3.2.1 LiDAR感測器 20 3.2.2 Intel RealSense追蹤攝影機T265 23 3.2.3 NVIDIA Jetson TX2開發套件 24 3.3 開放原始碼程式庫及軟體 25 3.3.1 Eigen 26 3.3.2 OpenMP API 26 3.3.3 Livox SDK 26 3.3.4 Intel RealSense SDK 2.0 27 3.3.5 PCL 27 3.3.6 Open3D 27 3.3.7 CloudCompare 28 第4章 LIDAR感測器之比較與選用 29 4.1 規格比較 29 4.2 掃描模式比較 30 4.3 實際測試 32 4.4 小結 42 第5章 系統開發與分析 43 5.1 模擬Livox LiDAR感測器之掃描模式 43 5.2 利用粒子群最佳化自動配置LiDAR感測器 46 5.2.1 粒子群最佳化之參數設定 46 5.2.2 利用平行計算加速粒子群最佳化 47 5.2.3 結果分析與比較 48 5.2.4 小結 56 5.3 LiDAR平台 59 5.3.1 配置LiDAR平台 59 5.3.1.1 硬體配置 59 5.3.1.2 位置與角度姿態之外參數標定(Extrinsic Calibration) 67 5.3.1.3 LiDAR感測器姿態誤差評估 70 5.3.2 自動化點雲收集程式 73 5.3.3 平台姿態追蹤 75 5.4 點雲資料後處理 76 5.4.1 點雲資料過濾 77 5.4.2 點雲套合(Registration) 77 第6章 實際測試 79 6.1 校內走廊 79 6.1.1 案例介紹 79 6.1.2 測試結果與討論 80 6.2 人行地下道 83 6.2.1 案例介紹 83 6.2.2 測試結果與討論 84 6.3 公路隧道 86 6.3.1 案例介紹 86 6.3.2 測試結果與討論 86 6.4 舊百吉隧道 89 6.4.1 案例介紹 89 6.4.2 測試結果與討論 90 6.5 舊五堵隧道 93 6.5.1 案例介紹 93 6.5.2 測試結果與討論 94 6.6 特徵物量測 95 6.7 感測器高度對於地面點雲之影響 98 6.8 追蹤攝影機T265產生異常資料 101 6.9 小結 102 第7章 結論與建議 103 7.1 結論 103 7.2 建議與未來展望 104 參考文獻 107

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