視覺姿態估測技術為機器人視覺定位系統中一個重要的核心技術，其目的為透過影像特徵點的移動資訊來估測相機本體在空間中的移動資訊。然而，此技術不但運算複雜度高，且容易因錯誤特徵匹配而影響估測準確度。本論文所提出之演算法即為解決使用RGB-D視覺感測資訊來估測相機移動時所面臨的技術問題，並提升估測相機三維旋轉角度及位移姿態之準確性及強健性。透過RGB-D影像中所偵測到的三維特徵匹配點，經由非線性最佳化方式來進行姿態估測運算，來求得相機於空間中的姿態資訊。為了提高系統運算效率，本論文亦經由Jacobian矩陣的整理來降低迭代的複雜度，藉此來加強系統整體的運算速度。本論文另加入M型估計式演算法抑制姿態估測演算法異常值的影響，以得出較穩健的結果。在實驗驗證部分，本論文使用實驗室所拍攝的數據以及Computer Vision Group網站[1]所提供的RGB-D影像的數據，比較三種現有之M型估計式之數學模型，並探討其對結果造成的影響。

Visual pose estimation technique, which estimates three-dimensional (3D) motion information of a camera system from changes of image features between adjacent frames, is an important core technology in vision-based robot localization systems. However, this technique usually is computationally expensive and is very sensitive to feature matching outliers. To address these technical problems, this thesis presents a RGB-D mapping algorithm that uses RGB-D visual sensing information to improve accuracy and robustness of six Degree-of-Freedom (6 DoF) motion estimation of the camera system. The proposed algorithm estimates the optimal 6 DoF posture information of the camera from the 3D feature matches between two RGB-D frames via a nonlinear optimization process. To improve the computational efficiency of the system, this thesis also derives Jacobian matrix associated with the cost function to reduce computational complexity of the optimization process, thereby enhancing overall system processing speed. Moreover, the proposed algorithm is combined with M-estimators to improve the robustness of the system against the influence of matching outliers. In the experiments, the performance of the proposed algorithm adopting three different types of M-estimators was studied by using RGB-D images corrected in our laboratory and provided on Computer Vision Group website [1].

目錄
中文摘要 I
英文摘要 II
目錄 III
圖目錄 V
表目錄 VI
第一章 序論 1
1.1 研究背景 1
1.2 研究動機與目的 4
1.3 論文架構 6
第二章 視覺姿態估測演算法 7
2.1 視覺姿態估測系統架構圖 7
2.2 姿態估測演算法 10
2.3 非線性最佳化演算法 17
2.4 Jacobian矩陣推導 22
2.5 座標轉換 32
第三章 M型估計式演算法 34
3.1 權重函式推導 34
3.2 權重函式之數學模型 36
第四章 實驗結果與分析 41
4.1 軟硬體系統架構 41
4.1.1 硬體介紹 44
4.1.2 RGB-D視覺軟體工具應用 47
4.2 實驗數據 48
4.3 實驗方式 50
4.4 實驗結果 54
4.4.1 Pan-Tilt實驗結果 54
4.4.2 freiburg2/xyz實驗結果 55
第五章 結論與未來展望 58
參考文獻	60

圖目錄
圖2.1、視覺姿態估測演算法示意圖。 9
圖2.2、提出的RGB-D視覺移動估測演算法。 10
圖2.3、非線性最佳化演算法流程圖。 22
圖4.1、軟硬體應用流程圖。 42
圖4.2、Microsoft Kinect RGB-D視覺攝影機。 44
圖4.3、Pan-Tilt Unit-D46控制平台。 46
圖4.4、Pan-Tilt實驗數據圖。 48
圖4.5、freiburg2/xyz實驗數據圖。 49
圖4.6、Pan-Tilt路徑示意圖。 51
圖4.7、freiburg2/xyz實際路徑圖。 53
圖4.8、實驗結果X-Y俯視圖。 57
圖4.9、實驗結果X-Z俯視圖。 57

表目錄
表2.1、數學公式參數化。 24
表2.2、一階微導矩陣。 25
表2.3、對當前x誤差的微分。 26
表2.4、對當前y誤差的微分。 27
表2.5、對當前z誤差的微分。 28
表2.6、對上一刻x誤差的微分。 29
表2.7、對上一刻y誤差的微分。 30
表2.8、對上一刻z誤差的微分。 31
表2.9、對C的微分。 32
表3.1、M型估計式演算法各模型 函式。 37
表3.2、M型估計式演算法各模型 函式和 函式。 37
表3.3、M型估計式演算法各模型模擬圖。 39
表3.4、本研究使用的 函式。 39
表4.1、Microsoft Kinect相關規格表。 45
表4.2、Pan-Tilt Unit-D46相關規格表。 46
表4.3、電腦規格表。 47
表4.4、Pan-Tilt實驗結果表。 55
表4.5、Pan-Tilt實際角度[0.0,0.0,0.0]座標表。 55
表4.6、Pan-Tilt實際位移[0.0,0.0,0.0]座標表。 55
表4.7、freiburg2/xyz位移實驗結果表。 56
表4.8、freiburg2/xyz角度實驗結果表。 56

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