A fast and robust simultaneous pose tracking and structure recovery algorithm for augmented reality applications

A robust simultaneous pose tracking and structure recovery algorithm based on the Interacting Multiple Model (IMM) for augmented reality applications is proposed in this paper. A set of three extended Kalman filters (EKFs), each describes a frequently occurring camera motion in real situations (general, pure translation, pure rotation), is applied within the IMM framework to track the pose of an object. Another set of EKFs, one filter for each model point, is used to refine the positions of the model features in the 3D space. The filters for pose tracking and structure refinement are executed in an interleaved manner. The results are used for inserting virtual objects into the original video footage. The performance of the algorithm is demonstrated with both synthetic and real data. Comparisons with different approaches have been performed and show that our method is more efficient and accurate.