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研究生:謝佳純
研究生(外文):Hsieh, Chia-Chun
論文名稱:MonocularVisionBasedSimultaneousLocalizationandMappingforaWheeledRobot
論文名稱(外文):利用單眼視覺於輪型機器人同步定位與環境地圖建立
指導教授:陳永昌陳永昌引用關係
指導教授(外文):Chen, Yung-Chang
學位類別:碩士
校院名稱:國立清華大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:英文
論文頁數:48
中文關鍵詞:即時定位機器人單眼視覺
外文關鍵詞:SLAMEKFMono
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在機器人研究領域,機器人同步定位與建立地圖(Simultaneous localization and mapping, SLAM)是個越來越受到重視的主題,SLAM要求當機器人被丟在未知位置且不知道週遭環境時,藉由觀察環境來建立前後一致的地圖,並自我定位於地圖中的位置。直覺想到的機器人里程器的資訊會有幫助,但里程器的量測誤差會一直累積;藉由別的量測儀器的幫助,像是超音波測距儀、攝影機,再由濾波器回授,SLAM可以做到更精確的地圖。SLAM最常用的是擴展的卡爾曼濾波器(Extended Kalman Filter)。
在這篇論文中,提出一個使用單眼視覺於輪型機器人同步定位與環境地圖建立的方法,只用一台網路攝影機的影像來修正里程器的數據。影像用 D. Lowe 提出的 SIFT 來取出特徵點並比對,當特徵點出現在和預測的地方不同,EKF利用其間的差距來修正建出的地圖。但修正時需要特徵點的深度資訊,無法提供深度是使用單眼視覺的最大問題,這篇使用 J. Civera 提出的深度倒數座標來表示特徵點的三維位置,這個座標表示法的好處是更符合EKF的線性個質,使EKF對特徵點的位置能更快地收斂。但 J. Civera 只有使用一個攝影機來進行即時定位與建立地圖,缺乏里程器回傳的資料,沒有速度的資訊是很可惜的,畢竟現在許多交通工具都具備里程器功能,這個系統結合了里程器,經過室內影像的測試,能提供了精確的地圖。

Simultaneous localization and mapping (SLAM) becomes popular in the autonomous robot research community. SLAM requests a mobile robot, placed at an unknown location in an unknown environment, to incrementally build a consistent map of this environment while simultaneously determining its location within this map. The odometer data is helpful but the noisy error accumulates. Assistant with measurements from the other sensors, SLAM improves the accuracy by filtering. The extended Kalman filter is the most used method to feedback the error.
In this thesis, a monocular vision based SLAM for a wheeled robot is proposed. A single web camera is the only measurement input to correct the odometer data. Extract features and match them based on the scale invariant feature transform (SIFT). Absence of the feature depth information is the key issue for the monocular vision in SLAM. The inverse depth parametrization method solves the problem by present the position in inverse coordinate with some reasonable assumptions. Convergence of the feature position is more easily achieved by the EKF due to the linearity. Combining the odometer data with the inverse depth method, this system provides an accurate map by tests of the indoor image sequence.

Table of Contents
Abstract i
Table of Contents ii
List of Figures iv
List of Tables v

Chapter 1: Introduction 1
1.1 Introduction to Simultaneous Localization and Mapping 1
1.2 Related Works 2
1.2.1 Extended Kalman Filter (EKF) and Particle Filter 2
1.2.2 Rangefinder Based Approaches and Vision Based Approaches 4
1.3 Thesis Organization 6

Chapter 2: Vehicle and Camera Models 7
2.1 Vehicle Model 7
2.2 Camera Pinhole Model 10
2.3 Camera Calibration 12

Chapter 3: Monocular Visual SLAM 15
3.1 Feature Extraction – SIFT 15
3.2 Extended Kalman Filter (EKF) Foundation 18
3.3 Feature Representation 21
3.4 Extended Kalman Filter (EKF) Steps 24
3.4.1 State Initialization 25
3.4.2 State Transition 28
3.4.3 Measurement Model 29
3.4.4 Feature Matching 31
3.4.5 Prediction and Update 31
3.5 State Management 33
Chapter 4: Experimental Results 35
4.1 Experimental Results of the Intermediate Steps 36
4.2 Experimental Results of the Image Sequences 38

Chapter 5: Conclusion and Future Works 43
5.1 Conclusion 43
5.2 Future Works 44

References 45


















List of Figures

Figure 2.1: U-bot mounted with Camera. 7
Figure 2.2: Possible Trajectories of differential steering. 8
Figure 2.3: The motion around the ICR. 8
Figure 2.4: Vehicle Coordinate. 8
Figure 2.5: Coordinates of the camera and the robot. 9
Figure 2.6: Camera Pinhole Model. 10
Figure 2.7: Practical Projection through Lens. 11
Figure 2.8: Different Types of Camera Radial Distortion. 11
Figure 2.9: Calibration Sequence. 13
Figure 3.1: SLAM Flowchart, without Initialization. 15
Figure 3.2: Illustration of Calculation of DoG and its extrema, from [31]. 17
Figure 3.3: Gradient and Orientation Histogram Forms the Descriptor. 18
Figure 3.4: Discrete Linear System. 19
Figure 3.5: Illustration of the Polar Coordinate. 22
Figure 4.1: Image Sequence 35
Figure 4.2: Calibration Results. 36
Figure 4.3: Feature Matching with SIFT (1). 37
Figure 4.4: Feature Matching with SIFT (2). 37
Figure 4.5: Feature Position Represented in XZ-Plane. 38
Figure 4.6: Robot Position in a Turn. 38
Figure 4.7: The turn Trajectory. 39
Figure 4.8: Round (1) Trajectory. 40
Figure 4.9: Round (2) Trajectory. 41
List of Tables
Table 4.1: Final Position Calculated from Different Methods of the turn. 39
Table 4.2: Final Position Calculated from Different Methods of Round(1). 40
Table 4.3: Final Position Calculated from Different Methods of Round(2). 41
Table 4.4: Run Time Comparison of these two Image Sequences. 42


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