Registration Based on Scene Recognition and Natural Features Tracking Techniques for Wide-Area Augmented Reality Systems

This research focuses on designing a robust and flexible registration method for wide-area augmented reality applications using scene recognition and natural features tracking techniques. Instead of building a global map of the wide-area scene, we propose to partition the whole scene into several sub-maps according to the user´s preference or the requirements of the augmented reality (AR) applications. Random classification trees are used to learn and recognize the reconstructed scenes because they naturally handle multi-class problems, while being both robust and fast. The result is a system that can deal with large scale scene that previous methods cannot cope with. We also propose a hybrid natural features tracking strategy combining both wide and narrow baseline techniques. While providing seamless registration, our system can recover from registration failures and switch between different sub-maps automatically. Experimental results demonstrate the validity of the proposed method for wide-area augmented reality applications.