Augmented reality based on online trifocal tensors estimation using multiple features

Abstract

Purpose

The purpose of this paper is to present a novel registration method for augmented reality (AR) systems based on robust estimation of trifocal tensor using point and line correspondence simultaneously.

Design/methodology/approach

The proposed method distinguishes itself in following three ways: first, to establish the world coordinate system, the restriction that the four specified points must form an approximate square is relaxed, the only requirement is that these four points should not be collinear. Second, besides feature points, line segments are also used to calculate the needed trifocal tensors. The registration process can still be achieved even without the use of feature points. Third, to estimate trifocal tensors precisely, progressive sample consensus (PROSAC) is used instead of random sample consensus to remove outliers.

Findings

As shown in the experiments, the proposed method really enhances the usability of this system. To calculate trifocal tensor, a PROSAC based algebraic minimization algorithm is put forward which improves the accuracy and reduces the computation complexity.

Research limitations/implications

In current system, it is stipulated that there is no large rotation of the user's head relative to the registration scenes, because the NCC will degrade when there is a large rotation between images.

Practical implications

A more robust feature matching strategy is needed. Treating feature matching as a classification problem may be a good choice.

Originality/value

This paper presents a novel registration approach for AR system.

Citation

Peng, C., Fangmin, D., Chunhua, Z. and Tao, G. (2009), "Augmented reality based on online trifocal tensors estimation using multiple features", Sensor Review, Vol. 29 No. 3, pp. 277-286. https://doi.org/10.1108/02602280910967693