A LoG Operator Based Markerless Augmented Reality Algorithm: LoG-PTAMM

The current markerless augmented reality（AR） algorithm has poor performance on 3D registration under various luminance and texture conditions. In order to enable the algorithm to overcome the drawbacks of robust problem in realistic environment, the LoG-PTAMM algorithm is proposed with Laplacian of Gaussian（LoG） operator. Firstly edges and details were intensified in AR scenario by filtering streaming image pyramids with LoG operator. Then the ZMSSD threshold of reprojection process had been set through the values of average of final accept score and average search length, since the frames contrast varied dramatically after LoG transformation. Besides a bilateral optical flow method was implemented for promoted matching precision. Finally an I_ls oriented relocalisation algorithm was implemented to search the Gaussian filtered video frames for camera pose recovery in case of tracking lost. Experimental performance was measured by average robustness index defined with hit rate and robustness, significant improvements was observed on illumination coherence and feature extraction in ordinary markerless AR scenarios in LoG-PTAMM algorithm comparing with PTAMM. Lifelong robust mapping and tracking in large scale environment become possible under multiple luminance and texture conditions.