真實物體的三維（3D）重建對無論是檢測、導航、醫療、逆向工程、安全性、物件辨識、可視化和動畫皆為非常重要且必要的研究領域。它可以通過不同的傳感器以不同的方法實現。事實上，如何選擇最佳的重建方法和傳感器並非容易的一件事情。它需要大量的經驗與試誤。在這項研究中，已經開發了三種3D重建應用。它們中的每一個都採用了不同的技術，傳感器和理論。

第一個應用程序以兩個校準的2D相機使用立體視覺理論識別和追蹤筆上的標記。在本研究中，已經可以精確地檢測並追蹤標記位置的中心。且演算法的限制和誤差模型已經推導完成。該應用程序可以在將來使用，但是將這些2D攝像機附加到機器人以跟踪人手軌跡以用於遠程過程。

第二個應用程序使用KINECT®傳感器來執行即時定位與地圖構建（SLAM）導航。本研究成功開發了適用於室內場景並基於圖形的SLAM。通過在環境中移動手持相機，程序可以檢測到環境中的特徵點，然後匹配這些特徵點並估算其3D位置。之後，對齊生成的3D雲並縫合其輪廓以構建此環境的3D地圖。該實驗可以應用在導航機器人上，使其能掃描各種室內環境，包括較危險的場景。

第三個應用程序重建伺服器主機的3D模型，並根據所選的虛擬攝像機位置呈現其2D圖像。然後，將真實的相機移動到這些選定位置並拍攝真實影像。透過比較真實和虛擬的圖像來評估系統準確性。這個想法是自動執行自動光學檢測（AOI）為一個真正的產

業案例。主要目標是檢查生產的伺服器主機上的組裝成果，標籤位置和零件放置位置是否正確。開發這種自動檢測系統將使檢查過程更快，更容易和更具經濟效益。

這些研究為工業領域的各種機器人視覺應用打開了大門。並同時針對特定的應用給予感測器與重建方式在選擇上的建議。

The three-dimensional (3D) reconstruction of real objects is a very important research sector required for inspection, navigation, medical and reverse engineering, security, object identifications, visualizations, and animations. It can be achieved via different methods through different sensors. In fact, choosing the optimal reconstruction method and sensor is not an easy task. It requires a lot of experience and trials and errors. In this work, three applications for 3D reconstruction have been developed. Each of them has been implemented with a different technique, sensor, and theory.

The first application recognizes and tracks a pen’s marker using stereo vision theory via two calibrated 2D cameras. The center of the marker position has been precisely detected and followed. Algorithm’s limitations and errors' models have been determined. This application could be utilized in the future through attaching these 2D cameras to a robot to follow human hand trajectory for remote processes.

The second application uses the KINECT® sensor to perform Simultaneous Localization and Mapping (SLAM) navigation. Graph-based SLAM has been developed in an indoor scenario. By moving a hand-held camera around the environment, features have been detected. Then, matching these features and estimating their 3D positions. After that, align the generated 3D clouds and close the loop to construct the 3D map for this environment. This experiment could be applied by a navigation robot to scan various indoor environments including dangerous ones.

The third application reconstructs the 3D model for a server and renders its 2D images in respect to selected virtual camera positions. Then, moves a real camera to these selected positions and captures real images. Comparing the real and virtual images, system accuracy has been assessed. This idea was automated to perform an Automatic Optical Inspection (AOI) for a real industry case. The main target is to inspect the assembling, labeling and component placement on a manufactured server. Developing such an autonomous inspection system makes the inspection process faster, easier and cost-efficient.

These studies open the door for various robotic vision applications in industrial fields. They also promote the functions of the sensors and reconstruction methods for specific applications.

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