本論文設計了一種應用於二軸馬達控制平台與輪型機器人移動平台的立體視覺里程計，可達到三軸旋轉與三軸位移的姿態估測效果。首先，本論文提出了一個估測立體視覺攝影機的內部參數方法，其可加強姿態估測資訊的準確性。此攝影機的內部參數是由立體視覺攝影機實際觀測到的資訊來估測，透過實際資訊得到的內部參數來做更佳符合攝影機的姿態估測。接著，透過估測到的攝影機內部參數，結合重新映射方法來設計一個有效的最佳姿態估測演算法，其使用立體視覺攝影機所觀測到的資訊來做連續影像間的累積姿態。本論文所提出的方法已經驗證在二軸馬達平台與輪型機器人姿態估測上，也驗證確實較一個現有演算法為佳。

This thesis proposes a stereo vision odometry algorithm used in two degree-of-freedom (DOF) pan-tilt and wheeled robot mobile platforms. The proposed algorithm achieves six-DOF camera pose estimation. To improve the accuracy of pose estimation, a simple and efficient calibration method is proposed to find the internal parameters of a stereo vision camera. Next, an optimal camera pose estimation algorithm is designed via a re-projection scheme based on the estimated camera internal parameters. Applying the proposed algorithm to a stereo vision camera system allows a platform equipped with the system actually estimating the pose change between two consecutive images, achieving the purpose of visual odometry. When applied on a two-DOF pan-tilt platform and a wheeled mobile robot, experimental results validate the performance of the proposed algorithm by comparing with an existing method.

目錄
中文摘要	I
Abstract	II
目錄	III
圖目錄	IV
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	5
第二章	 實驗系統介紹	6
2.1	 硬體介紹	6
2.2	 立體視覺軟體工具應用	10
第三章	 視覺里程計相關演算法	12
3.1	 本質矩陣方法	12
3.1.1	基本矩陣與內部參數矩陣	12
3.1.2	本質矩陣方法	14
3.2	 RANSAC移動估測方法	16
第四章	 立體視覺里程計演算法	22
4.1	 視覺里程計前處理	23
4.1.1	攝影機內部參數估測	23
4.2	 提出之視覺里程計演算法	27
4.2.1	特徵提取演算法	28
4.2.2	離群點移除演算法	31
4.2.3	最佳化重新投影(Re-projection)誤差	33
4.3	 姿態更新	39
4.4	 系統架構圖	41
第五章	 實驗結果與分析	44
5.1	 攝影機內部參數估測	45
5.2	 攝影機姿態估測實驗	49
參考文獻	59

圖目錄
圖2.1、Bumblebee2立體視覺攝影機。	7
圖2.2、Pan-Tilt Unit-D46控制平台。	8
圖2. 3、FiveBOT 004 輪型機器人平台	9
圖2.4、軟硬體應用流程。	10
圖3.1、三維映射轉換動作。	13
圖3.2、本質矩陣方法流程圖。	16
圖3.3、RANSAC演算法流程圖。	17
圖3.4、RANSAC移動估測方法流程圖。	21
圖4.1、所提出之方法主要流程圖。	22
圖4. 2、二維平面圖形參考	24
圖4.3、SIFT主要流程圖	29
圖4.4、特徵點賦予主方向示意圖。	29
圖4.5、描述子建立示意圖。	30
圖4.6、特徵點匹配與離群點移除結果圖。	33
圖4. 7、輪型載具與攝影機表示圖	39
圖4. 8、輪型載具行進示意圖	39
圖4.9、本論文演算法系統架構圖。	41
圖5. 1、三軸旋轉與三軸方向示意圖	46
圖5.2、攝影機內部參數矩陣估測流程圖	46
圖5.3、攝影機內部參數估測場景：(a)場景一，(b)場景二，(c)場景三，(d)場景四。	47
圖5. 4、實驗的實際軌跡：(a)實驗A，(b)實驗B。	51
圖5.5、提出的方法與RANSAC移動估測方法之均方誤差比較：(a)實驗A的均方誤差比較圖，(b)實驗B的位移均方誤差比較圖，(c)實驗B的旋轉均方誤差比較圖。	55
圖5. 6、提出之方法與RANSAC方法實驗結果之軌跡路徑	57

表目錄
表2.1	Bumblebee2相關規格需求表	9
表2.2	Pan-Tilt Unit-D46相關規格表	9
表2. 3	FiveBOT004 輪型機器人平台規格	10
表2.4	電腦規格表	10
表4.1	四種特徵提取方法的比較。	28
表5.1	攝影機內部參數統計與平均	48
表5.2	實驗比較誤差百分比	55
表5. 3	實驗偏差值比較	55

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