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Building Performance Visualization Using Augmented Reality

By Ali M. Malkawi and Ravi S. Srinivasan


Magnetic motion trackers have been widely used for tracking user head / hand pose information owing to their advantages such as size, occlusion-less tracking environment and high sample rate. Yet, issues such as latency and jitter leave magnetic tracker technology unfavorable as compared to other tracker technologies. While latency is due to tracker hardware employed, jitter is due to magnetic field distortion caused by the presence of metals nearby. These issues permit the emergence of registration errors when employed for Virtual Environment (VE) systems, specifically for interactive Virtual Reality (VR) or Augmented Reality (AR) applications. This paper discusses the integration of prediction-smoothing algorithms to achieve accurate registration for a magnetic tracker-based immersive Augmented Reality – Computational Fluid Dynamics (CFD) environment. In this project, Kalman and Gaussian filters are utilized to remove latency and jitter effects. In addition, to allow efficient control of head-pose data prediction, a control variable is appended to the Kalman dynamic equation. Furthermore, to permit real-time latency calibration during immersive visualization, speechrecognition is integrated with the system. Such integration, apart from enabling accurate calibration of AR setup, permits robust on-the-fly latency calibration, thereby facilitating effective user experience during interactive, immersive AR visualization of CFD datasets of indoor spaces

Topics: Magnetic motion tracker, registration, augmented reality, Kalman filter
Year: 2011
OAI identifier: oai:CiteSeerX.psu:
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