| Summary: | Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020 === Cataloged from the official PDF of thesis. === Includes bibliographical references (pages 111-125). === Seamless perception of objects' physical properties, such as temperature, is a key to improving the way we live and work. Thanks to the rapid development of sensor technology, Internet of Things (IoT) is shaping our world by expanding digital connectivity to real objects. In this way, physical properties of objects can be effectively collected, processed, transmitted and shared. Yet, only being able to sense the surrounding environment is not enough: A user-friendly way to visualize information is also required. Today, Augmented Reality (AR), which overlays digital information onto physical objects, is growing fast, and has been adopted successfully in many fields. This thesis focuses on fusing advantages of various technologies to create a better IoT experience in AR environment. === First, we describe an integrated system to enhance users' IoT experience in AR environments: Users are allowed to directly visualize objects' physical properties and control IoT devices in an immersive manner. This system is able to localize in-view target objects based on their natural appearances without using fiducial markers, such as QR codes. In this way, a more seamless user experience can be achieved. Second, existing handcrafted computer vision methods can estimate objects' poses only for simple cases (i.e. textured patterns or simple shapes), and usually fail for complex cases. Recently, deep learning has shown promise to handle various tasks in a data-driven approach. In this thesis, 3D deep learning models are explored to estimate objects' pose parameters in a more accurate manner. Hence, better robustness and accuracy can be achieved to support IoT-AR applications. === Third, standard deep learning training pipeline for object pose estimation is supervised, which requires ground truth pose parameters to be known. Manually obtaining such data is time consuming and expensive, making it hard to scale. As the last contribution, methods using synthetic data are studied to automatically train object pose estimation models without human labelling. === by Yongbin Sun. === Ph. D. === Ph.D. Massachusetts Institute of Technology, Department of Mechanical Engineering
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