Dual-user localization using ARKit and nearby interaction for iOS devices - Zhang, Yuyang (Nanyang Technological University, 2024)
This dissertation delves into the burgeoning field of indoor ranging and dualuser Augmented Reality (AR) techniques, primarily focusing on the integration and comparison of Visual Inertial Odometry (VIO) and Ultra-Wideband (UWB) technologies. The motivation behind this research stems from the need to improve accuracy and stability in indoor ranging systems, particularly for dual-user AR applications. The study systematically explores the advantages and limitations of VIO and UWB localization in various indoor environments, employing rigorous experimental methods to assess their performance under different conditions. Significant emphasis is placed on the development and application of a Kalman filter-based approach to fuse the data from both VIO and UWB ranging, aiming to enhance the overall accuracy and stability of indoor ranging. The results demonstrate notable improvements in distance estimation precision and positioning stability, highlighting the potential of this integrated approach for future dual-user AR applications. This work not only contributes to the advancement of indoor ranging technologies but also sets a foundation for further exploration in dual-user AR environments.