臺灣博碩士論文加值系統

近年來由於科技的進步以及社群網站日漸普及，目前幾乎人手一台智慧型手機。許多人都樂於上傳自己所拍攝之影像於網路上與大眾分享，若要得到一陌生環境之歷史影像與資訊已非難事。因此當身處於一陌生室內環境時，若能利用人們上傳至網路雲端中既有的大量歷史影像以達到定位與建圖的目的，則能減少建立資料庫與處理大量資訊所耗費的成本。
本篇論文研究目的是藉由影像處理系統來協助即時相機之定位與建圖，其包含一台擷取影像之RGB-D 深度相機以及處理運算分析之電腦。首先在影像前處理部分，我們將即時相機座標系定義為世界座標系，透過歷史影像與即時影像之特徵點比對以取得投影模型，並且藉由座標系定義和相機校正之特性，解析出歷史影像資料庫相對於即時相機之位置與角度，再利用座標轉換得到即時相機在世界座標之相對資訊。在定位與建圖的部分，本文是採用擴展卡爾曼濾波器。藉由此相對資訊所得出之觀測模型，可以令EKF-SLAM 估測器對即時相機之狀態產生穩定的估測結果，對影像資料庫能進行更新並且能有良好的收斂，也能建構出即時相機之路徑與地圖。
本論文之貢獻為不需與一般SLAM 相同藉由匹配在連續影像中相似度高的同一特徵，而是直接找出兩張影像間之相對狀態，以減少在搜尋上的時間成本。另外，由於實驗儀器是使用RGB-D 測距相機，與一般使用兩張普通影像再去求解出三維特徵點，更能直接獲取三維點，並且能減少座標轉換的次數，而用更快的方法得到即時影像與資料庫影像之相對狀態。本論文在應用上能利用未知環境中既已存在之影像資料庫，透過RGB-D 即時相機來實現定位。

Recently, due to the advances in technology and the growing popularity of social network, almost everyone owns a smartphone now. Most of people are happy to upload the photographs they took in the internet and share with others. It is easy to get the historical
images and the image’s information for an unfamiliar environment. Therefor,when we are in an unfamiliar environment, if we can use the numerous historical images which were upload by people in cloud network to achieve the purpose of localization and mapping, then can reduce the cost of creating database and processing large amounts of information.
The purpose of this paper is to depend on a computer vision system to assist the simultaneous localization and mapping for a realtime camera, which includes a RGB-D depth camera for capture images and a computer for processing computing analysis. In the image pre-processing part, we first assume that real-time camera coordinate is world coordinate, then via matching the feature points between historical images and realtime images, we can get the projection model. By the definition of coordinate system and the property of camera calibration, we can get the information of position and angle for historical image database relative to the realtime camera, and get the relative information of realtime camera in world coordinate with the coordinate transformation. In the localization and mapping part, we use the Extended Kalman filter SLAM estimator to generate a stabilize measurement result for the state of realtime camera, and image database can get a great convergence result, then can create the path and map for the realtime camera.
Our contribution of this thesis is we don’t have to match the high similarity features in the continuous image like the general SLAM, we can directly find the relative state between two images instead, and it can reduce the time cost in finding features. In additionally, due to our experimental equipment is RGB-D camera, so we don’t have to use two ordinary image to find 3D features. Instead we can directly get the 3D information and can reduce the number of coordinate transformation and get the relative state between realtime image and database image faster. In this paper, our application is that we can use the exist image database in the unknown area and a RGB-D realtime camera to achieve the positioning.

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
1 緒論 1
1.1 前言 1
1.2 研究動機與目標 1
1.3 文獻回顧 2
1.4 論文架構 3
2 影像處理基礎架構 4
2.1 相機幾何 4
2.1.1 相機模型 4
2.1.2 相機參數 5
2.2 投影與校正 7
2.2.1 相機投影矩陣 7
2.2.2 Golden Standard Algorithm 9
2.2.3 相機校正 10
2.3 座標系統 13
2.3.1 各座標系定義 13
2.3.2 影像資料庫與即時相機座標定義 14
2.3.3 座標系轉換 14
2.4 相機位置 17
3 影像同步定位與建圖 20
3.1 擴展卡爾曼濾波器 20
3.2 運動模型 23
3.2.1 二維環境下之狀態方程式 24
3.2.2 二維環境下之量測方程式 25
4 實驗結果與分析 28
4.1 實驗設備 28
4.2 實驗步驟 29
4.2.1 對應特徵點選取 31
4.2.2 求取投影矩陣與轉移矩陣 33
4.2.3 求取相對資訊 36
4.2.4 求取座標轉換資訊 37
4.3 路徑估測與建圖 38
5 結論與未來展望 46
5.1 成果討論 46
5.2 未來展望 47
參考文獻 48

[1] H. C. Longuet-Higgins, “Readings in computer vision: Issues, problems, principles, and paradigms,” ch. A Computer Algorithm for Reconstructing a Scene from Two Projections, pp. 61–62, San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1987. 2
[2] P. J. Besl and N. D. McKay, “A method for registration of 3-d shapes,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 14, pp. 239–256, Feb. 1992. 2
[3] O. Faugeras, Three-dimensional Computer Vision: A Geometric Viewpoint. Cambridge, MA, USA: MIT Press, 1993. 2
[4] Z. Zhang, “A flexible new technique for camera calibration,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, pp. 1330–1334, Nov. 2000. 2
[5] H. Durrant-Whyte and T. Bailey, “Simultaneous localization and mapping: part i,” Robotics Automation Magazine, IEEE, vol. 13, pp. 99–110, June 2006. 2
[6] T. Bailey and H. Durrant-Whyte, “Simultaneous localization and mapping (slam): part ii,” Robotics Automation Magazine, IEEE, vol. 13, pp. 108–117, Sept 2006. 2
[7] A. Davison, I. Reid, N. Molton, and O. Stasse, “Monoslam: Real-time single camera slam,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 29, pp. 1052–1067, June 2007. 2
[8] 葉科廷, 單攝影機3D 視覺定位. PhD thesis, 元智大學, 桃園縣, 2007. 3
[9] 阮堯輝, 使用單一相機全向輪機器人之同時定位建圖與局部路徑規劃研究.PhD thesis, 中興大學, 台中市, 2008. 3
[10] 洪祥恩, 以地面及空載光達點雲重建複雜物三維模型. PhD thesis, 國立中央大學, 桃園縣, 2011. 3
[11] P. Henry, M. Krainin, E. Herbst, X. Ren, and D. Fox, “Rgb-d mapping: Using kinectstyle depth cameras for dense 3d modeling of indoor environments,” Int. J. Rob. Res., vol. 31, pp. 647–663, Apr. 2012. 3
[12] 胡皓翔, 運用街景影像資料庫的智慧手機定位. PhD thesis, 國立臺灣科技大學,台北市, 2013. 3
[13] R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision. New York, NY, USA: Cambridge University Press, 2 ed., 2003. 4, 9