More improved robust orthogonal iterative algorithm for pose estimation in AR

Jin-Tao Ma; Ya Zhou; Wei Liu; Qun Hao

doi:10.1117/12.791589

5 March 2008 More improved robust orthogonal iterative algorithm for pose estimation in AR

Jin-Tao Ma, Ya Zhou, Wei Liu, Qun Hao

Proceedings Volume 6623, International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing; 662326 (2008) https://doi.org/10.1117/12.791589
Event: International Symposium on Photoelectronic Detection and Imaging: Technology and Applications 2007, 2007, Beijing, China

Abstract

Estimation of camera pose is an integral part and classical problem of augmented reality (AR) system and computer vision. Accurate pose estimation is crucial in determining the rigid transformation relating 2D images to known 3D geometry. Therefore, the algorithm should be not only fast and accuracy, but also robust in AR system. Orthogonal iterative (OI) algorithm is a good method, but it requires a proper initialization and cannot deal with problems of pose ambiguity. A new method based on OI we presented before, provides a good initialization and solves a problem of pose ambiguity introduced by coplanar markers. However, two more potential problems usually make the algorithm calculate some wrong results, and lead to the algorithm unsteady and not robust. In this paper, we develop the method by resolving pose ambiguities, which originate from potential problems in algorithm. Two more constraints are employed in our method. One is camera must be located in front of the marker, while the other is camera must be oriented to the marker. It's proved that the improved method is steady in experiments, and can calculate the pose of camera fast and correctly. Moreover, since the method can deal with pose ambiguity, it is rather robust in AR system.

Citation Download Citation

Jin-Tao Ma, Ya Zhou, Wei Liu, and Qun Hao "More improved robust orthogonal iterative algorithm for pose estimation in AR", Proc. SPIE 6623, International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing, 662326 (5 March 2008); https://doi.org/10.1117/12.791589

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