Advancing Augmented Reality: Localization Algorithm Analysis and Pre-Positioning Virtual Objects
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| Publicat a: | ProQuest Dissertations and Theses (2025) |
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| Accés en línia: | Citation/Abstract Full Text - PDF |
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| Resum: | This thesis evaluates localization algorithms for Augmented Reality (AR) applications, focusing on five state-of-the-art monocular-inertial localization algorithms— OpenVINS, VINS-Mono, ORB-SLAM3, Kimera-VIO, and DM-VIO. These algorithms were assessed using publicly available datasets (EuRoC) and custom datasets collected with handheld devices, simulating typical AR user movements. The evaluation highlights trade-offs in accuracy, robustness, and initialization time, providing insights into their suitability for various AR scenarios. A comparative analysis with Google’s ARCore reveals that while custom algorithms have higher precision in outdoor environments, ARCore demonstrates superior precision indoors.A significant contribution of this work is the development of an AR pipeline capable of accurately rendering virtual assets in their intended real-world locations without relying on pre-existing 3D maps. The pipeline comprises four threads: data capture, origin setting, localization, and rendering. It incorporates fiducial markers such as AprilTags to seamlessly align the real and virtual worlds by establishing a shared origin between them. |
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| ISBN: | 9798311909402 |
| Font: | ProQuest Dissertations & Theses Global |