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系統識別號 U0026-0608201815123600
論文名稱(中文) 地面目標物識別及相對定位視覺演算法
論文名稱(英文) Vision Algorithm for Target Recognition and Pose Estimation
校院名稱 成功大學
系所名稱(中) 航空太空工程學系
系所名稱(英) Department of Aeronautics & Astronautics
學年度 106
學期 2
出版年 107
研究生(中文) 謝秉勳
研究生(英文) Ping-Hsun-Hsieh
電子信箱 deutsch2630@gmail.com
學號 P46041518
學位類別 碩士
語文別 中文
論文頁數 68頁
口試委員 指導教授-賴維祥
口試委員-曾義星
口試委員-饒見有
中文關鍵字 無人飛行載具  目標偵測  電腦視覺  視覺導航降落 
英文關鍵字 UAV  Vision-based Detection  Computer Vision  Autonomous Landing 
學科別分類
中文摘要 本研究針對地面目標物精準定位,利用SURF以特徵匹配方式偵測地面目標物,定義目標物座標系將影像的對應點透過內方位參數投影到相機座標系,同時RANSAC以Homography作為模型剔除不可靠的對應點,計算座標系間的Homography轉換矩陣,最後解Homography參數得到在目標物座標系相機對地面目標物六個自由度參數的相對關係(平移與旋轉),只要影像與目標有足夠的對應點,無論周圍有無雜物,都可以成功辨識目標物與相機與計算目標物的相對方位。以UAV掛載相機及飛時(ToF)測距感測器偵測高度討論成果,飛行高度與相機解析度直接影響匹配成果,在解析度足夠且獲得足夠特徵點的情況降低運算階層可以不影響計算Homography轉換成果增加運算效能。未來可以延伸應用在UAV降落、目標物追蹤、目標定位執行投擲任務,多架無人機停機坪設計等等。
英文摘要 SURF is used to search correspondences between image captured by camera and the ground target. No matter how the environment changes, as long as enough correspondences are matched, the target can be recognized successfully. Project correspondences from image coordinate to camera coordinate by using intrinsic matrix. RANSAC based on Homography transform as model is used to discard outliers. After verifying geometric relationship of projected shape between two coordinates via Homography matrix, external parameters are obtained by decomposing Homography matrix. The relative position between camera and the target would be known. UAV equipped with downward camera and time of flight rangefinder which is used to measure height information flies at the different altitude to verify the algorithm. The number of correspondences is influenced by the camera resolution and flight altitude directly. At appropriate camera resolution and flight altitude, the compute efficiency increases robustly by reducing octave number.
The vision algorithm can be used to guide UAV landing autonomously, target tracking or goods delivery precisely in the future.
論文目次 中文摘要 I
Vision Algorithm for Target Recognition and Pose Estimation II
致謝 VI
內文目錄 VII
表目錄 IX
圖目錄 X
符號 XIV
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 5
1.3 文獻回顧 5
1.4 研究方法與目的 13
1.5研究貢獻 14
1.6 論文架構 14
第二章 電腦視覺演算法 15
2.1 顏色模型 15
2.1.1 RGB顏色模型 [26] 15
2.1.2 灰階 16
2.2 相機模型 17
2.2.1內方位參數 17
2.2.2外方位參數 18
2.2.3齊次座標系 18
2.3相機校正 19
2.3.1透鏡畸變 19
2.3.2相機校正 21
2.4 加速強健特徵 25
2.4.1特徵點偵測 27
2.4.2特徵點描述子與匹配 31
2.4.3快速索引匹配 33
2.5 單應性轉換 33
2.5.1何謂單應性? 34
2.5.2 DLT演算法基礎 34
2.6成果檢驗 38
第三章 實驗設備介紹 39
3.1視覺系統設備 41
3.2飛行載具設備 44
第四章 實驗方法與測試 47
4.1相機校正 47
4.2加速強健特徵驗證 50
4.3室內測試 52
4.4飛行測試 54
4.5加速強健特徵應用討論 56
4.6檢驗外方位參數 59
第五章 結論與未來工作 63
5.1結論 63
5.2未來工作 64
參考文獻 65
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