Markerless tracking for augmented reality applied in reconstruction of Yuanmingyuan archaeological site

This paper presents an algorithm based on the method of supervised machine learning and multi-keyframes to achieve markerless augmented reality (AR) application when there is a locally planar object in the scene. The main goal is to solve the problem of AR tracking in outdoor environment by only using vision and natural features. Instead of tracking fiducial markers, we track natural keypoints, during which the point correspondences are established from the classification perspective. The tracking range is able to be extended by employing many reference images. These results in a promising algorithm that successfully tested on a touring guide system, which provides views of virtual original appearance superimposed to ruins of Yuanmingyuan archaeological site of China. Comparisons are also made between ARToolkit and the proposed algorithm in indoor environment. Experimental results demonstrated that our algorithm is characterized by fairly robustness and high time efficiency in both indoor and outdoor application.