Registration using adaptive particle filter and natural features matching techniques for augmented reality systems

Purpose - The purpose of this paper is to provide a flexible registration method for markerless augmented reality (AR) systems. Design/methodology/approach - The proposed method distinguishes itself as follows: firstly, the method is simple and efficient, as no man-made markers are needed for both indoor and outdoor AR applications. Secondly, an adaptation method is presented to tune the particle filter dynamically. The result is a system which can achieve tolerance to fast motion and drift during tracking process. Thirdly, the authors use the reduced scale invariant feature transform (SIFT) and scale prediction techniques to match natural features. This method deals easily with the camera pose estimation problem in the case of large illumination and visual angle changes. Findings - Some experiments are provided to validate the performance of the proposed method. Originality/value - The paper proposes a novel camera pose estimation method based on adaptive particle filter and natural features matching techniques.