本篇論文利用深度攝影機重建點雲環境，取得環境資訊作定位。研究方法主要分成三個階段：建置點雲環境、虛擬照片拍攝及資料庫建置、特徵點定位，建置點雲環境的階段主要利用RANSAC 作初步的點雲重合，在利用Graph Slam 作拍攝路徑的最佳化，使得深度重合能夠擺放在理想的位置。藉由虛擬照片建置的影像資料庫，能夠取得一般相機所缺乏的定位資訊，像是距離景物較遠的距離，抑或是被障礙物遮蔽所導致特徵資訊缺乏的情形發生。在最後特徵點定位的階段，利用SIFT 將虛擬相片與代訂為相片作比對，求出的特徵點根據位置及其夾角進行三角定位。在實驗中，我們方法改善了定位成功的覆蓋率，以及平均誤差，說明照片的拍攝位置與角度，都會影響定位資訊的取得，藉由改善相機分布與角度的情況，能夠有效的減低定位誤差。

This paper develops an approach for image triangulation from point cloud.
This approach can be divided into three parts: reconstructing environment, virtual images database establishment and triangulation. During constructing virtual images database, we can acquire extra localization information which traditional image localization lacks. When camera is far from scene or camera is sheltered by objects, traditional SIFT localization may decrease the accuracy. Our approach provides higher localization accuracy and coverage ratio by choosing better camera angles and positions automatically. In experiments, we take practical localization by traditional SIFT localization and virtual images triangulation to compare result.

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