Registration based on Euclidean reconstruction and natural features tracking for augmented reality systems

Abstract

PurposeThe purpose of this paper is to propose a novel registration method using Euclidean reconstruction and natural features tracking for AR‐based assembly guidance systems.Design/methodology/approachThe method operates in two steps: offline Euclidean reconstruction and online tracking. Offline stage involves obtaining the structure of scene using Euclidean reconstruction technique. The classification trees are constructed using affine transform for online initialization. In tracking, the classification‐based wide baseline matching strategy and Td,d test are used to get a fast and accurate initialization for the first frame after which a modified optical flow tracker is used to fulfill the task of feature tracking in the real‐time video sequences. The four specified points are transferred to the current image to compute the registration matrix for augmentation.FindingsFirstly, Euclidean reconstruction was used instead of projective reconstruction to get the projections of predefined features. Compared with the six points needed in projective reconstruction‐based method, this method can run normally even when only four features are successfully tracked. Secondly, an adaptive strategy was proposed to adjust the classification trees using the tracked features in online stage by which one can initialize or reinitialize the system, even with large difference between the first and reference images.Originality/valueSome indoor and outdoor experiments are provided to validate the performance of the proposed method.