Fast and Scalable Structure-from-Motion for High-precision Mobile Augmented Reality Systems

A key problem in mobile computing is providing people access to necessary cyber-information associated with their surrounding physical objects. Mobile augmented reality is one of the emerging techniques that address this key problem by allowing users to see the cyber-information associated with real-world physical objects by overlaying that cyber-information on the physical objects ’ imagery. As a consequence, many mobile augmented reality approaches have been proposed to identify and visualize relevant cyber-information on users ’ mobile devices by intelligently interpreting users ’ positions and orientations in 3D and their associated surroundings. However, existing approaches for mobile augmented reality primarily rely on Radio Frequency (RF) based location tracking technologies (e.g., Global Positioning Systems or Wireless Local Area Networks), which typically do not provide sufficient precision in RF-denied areas or require additional hardware and custom mobile devices. To remove the dependency on external location tracking technologies, this dissertation presents a new vision-based context-aware approach for mobile augmented reality tha