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研究生:彭翊雁
研究生(外文):Yi-Yen Peng
論文名稱:以強度資訊進行多時期車載光達點雲特徵匹配與改正
論文名稱(外文):Feature Matching and Adjustment for Multi-Epoch Mobile LiDAR Point Clouds Based on Reflective Intensities
指導教授:韓仁毓韓仁毓引用關係
指導教授(外文):Jen-Yu Han
口試委員:劉啟清張智安黃智遠
口試委員(外文):Chi-Ching LiuTee-Ann TeoChih-Yuan Huang
口試日期:2015-06-22
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:土木工程學研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:90
中文關鍵詞:車載光達系統慣性導航系統反射強度值影像增顯影像匹配自適應分段改正
外文關鍵詞:Mobile LiDAR systemInertial navigation system (INS)IntensityImage enhancementImage matchingAdaptive time-variant adjustment
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車載光達系統於衛星訊號受到遮蔽時,由於慣性導航系統獨立估算載具位置與姿態之誤差將隨時間累積,使得點雲資料之誤差行為不僅具有累積性也與訊號遮蔽時段有關,因此須對點雲進行自適應分段改正。本研究採用路面標線特徵作為自適應分段改正之共軛點,由萃取之路面點雲產製強度影像,並利用影像增顯提升路面特徵之辨識度與對比度,再由影像共軛匹配技術獲取共軛特徵點並反投影回三維空間,以動態門檻之粗差偵測策略提升共軛匹配點之正確性,最後,進入自適應分段改正之流程。實驗成果顯示,所提出的方法可提供精確且足夠的共軛特徵,以利後續自適應分段改正,而多時期點雲間之誤差則可由數十公分之誤差下降至公分等級,能有效消除衛星訊號遮蔽造成之誤差。

Mobile LiDAR systems have been widely used in collecting 3D spatial information due to its high efficiency. However, its positioning quality relies on a good reception of GNSS signals. Therefore, point clouds positioning error accumulates with time when GNSS signal is obstructed. This study presents a fully automatic intensity-based multi-epoch mobile LiDAR point clouds matching and adjustment procedure. The experiment results indicated that the proposed frameworks can provide accurate and sufficient features for the adaptive time-variant adjustment and help to improve the positioning quality for the LiDAR point clouds acquired in a GNSS signal obstructed area. Consequently, the mobile LiDAR systems can be extended to a wider field of applications.

口試委員審定書 i
中文摘要 ii
Abstract iii
目錄 iv
圖目錄 vi
表目錄 ix
第一章 緒論 1
1.1 研究背景 1
1.1.1 移動式測繪系統 1
1.1.2 光達掃瞄系統 3
1.2 研究動機 6
1.3 研究目的 9
1.4 論文架構 10
第二章 文獻回顧 11
2.1 車載光達系統 11
2.1.1 定位定向感測器 11
2.1.2 三維雷射掃瞄技術 12
2.1.3 車載坐標系統 13
2.1.4 車載光達系統的應用 14
2.2 車載光達系統定位誤差及動態改正 16
2.2.1 定位誤差來源與處理策略 16
2.2.2 光達點雲套合 18
2.2.3 自適應分段改正 23
2.3 車載光達點雲路面特徵匹配 24
2.3.1 路面點雲萃取 25
2.3.2 點雲網格化 29
2.3.3 點雲輻射值改正 30
2.3.4 影像特徵偵測與匹配 34
2.4 小結 39
第三章 研究方法 41
3.1 研究流程 41
3.2 資料預處理 42
3.2.1 軌跡資料分割 42
3.2.2 點雲資料分割 43
3.3 路面點雲萃取 45
3.3.1 初步高程濾除 45
3.3.2 隨機抽樣一致性演算法濾除 46
3.4 強度影像產製 48
3.4.1 點雲網格化 48
3.4.2 影像增顯 50
3.5 共軛特徵偵測與匹配 52
3.5.1 特徵偵測 52
3.5.2 特徵匹配與像空間粗差偵測 54
3.5.3 三維共軛點雲與物空間粗差偵測 55
3.6 自適應分段改正 57
第四章 實驗與成果分析 60
4.1 資料介紹 60
4.2 資料預處理 61
4.3 路面點雲萃取成果 62
4.4 強度影像產製成果 64
4.5 共軛特徵偵測與匹配 67
4.5.1 影像增顯前後特徵偵測成果比較 67
4.5.2 影像增顯前後特徵匹配與像空間粗差偵測成果比較 70
4.5.3 三維共軛點雲與物空間粗差偵測成果 74
4.6 自適應分段改正成果 76
第五章 結論與建議 80
5.1 結論 80
5.2 建議 82
參考文獻 84


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