Wearable computers afford a degree of mobility that makes tracking for augmented reality difficult. This paper presents a novel object-centric tracking architecture for presenting augmented reality media in spatial relationships to objects, regardless of the objects ’ positions or motions in the world. The advance this system provides is the ability to sense and integrate new features into its tracking database, thereby extending the tracking region automatically. A lazy evaluation of the structure from motion problem uses images obtained from a single calibrated moving camera and applies recursive filtering to identify and estimate the 3D positions of new features. We evaluate the performance of two filters; a classic Extended Kalman Filter (EKF) and a filter based on a Recursive-Average of Covariances (RAC). Implementation issues and results are discussed in conclusion. 1.
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