An Optimization Study Of Augmented Reality Geometric Consistency

Augmented Reality is a newly developed fusion actual situation,mainly through a virtual object superimposed on a real scene information of image enhancement,has in-depth research in the military,medical,industrial and entertainment fields.Augmented Reality solve geometric consistency of the various parts of the system need to cooperate with each other,such as camera tracking,3D registration target,geometric scene reconstruction.The image feature point is to connect the various processes of the bridge augmented reality,robustness and real-time directly related to their algorithms to enhance the performance of real systems.Therefore,the study and optimization of feature points algorithm becomes the key to solving the geometric consistency of augmented reality.It can be applied to the image feature point camera tracking camera imaging principle by extracting feature points,matching and tracking of its basic operations.Through research,we found Harris,SUSAN and FAST algorithms can not meet the augmented reality system robustness requirements;SIFT and SURF algorithm augmented reality system can not meet the real-time requirements;paper SURF algorithm,according to E-FAST algorithm advantages in terms of real time,on the SURF algorithm acceleration optimization.In the feature point matching study,this paper Euclidean distance as the similarity measure,use kd-tree index data,and through RANSAC algorithm eliminate false matching feature points;the feature point tracking,optical flow algorithm paper KLT based on the introduction of local adaptive algorithm optimization;algorithm and optimization for the above article are theoretical analysis and experimental comparison.The image feature points related algorithms involve a large number of floating-point operations and convolution-based hardware platform CPU is difficult to ensure enhanced real-time reality system.The use of GPU and DSP processor in image processing advantages and parallel structures,and feature points in the pixel level for SURF and KLT algorithm parallelization accelerated algorithm optimization purposes.Finally,the use of augmented reality tools to achieve specific ARToolKit routine development.