A stable and accurate marker-less augmented reality registration method

Authors: Gao, Q., Wan, T.R., Tang, W. and Chen, L.

http://eprints.bournemouth.ac.uk/29499/

Start date: 20 September 2017

Markerless Augmented Reality (AR) registration using the standard Homography matrix is unstable, and for image-based registration it has very low accuracy. In this paper,we present a new method to improve the stability and the accuracy of marker-less registration in AR. Based on the VisualSimultaneous Localization and Mapping (V-SLAM) framework,our method adds a three-dimensional dense cloud processingstep to the state-of-the-art ORB-SLAM in order to deal withmainly the point cloud fusion and the object recognition. Ouralgorithm for the object recognition process acts as a stabilizer toimprove the registration accuracy during the model to the scenetransformation process. This has been achieved by integrating theHough voting algorithm with the Iterative Closest Points(ICP)method. Our proposed AR framework also further increasesthe registration accuracy with the use of integrated cameraposes on the registration of virtual objects. Our experiments show that the proposed method not only accelerates the speed of camera tracking with a standard SLAM system, but also effectively identifies objects and improves the stability of marker-less augmented reality applications.

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Authors: Gao, Q.H., Wan, T.R., Tang, W. and Chen, L.

http://eprints.bournemouth.ac.uk/29499/

Journal: Proceedings - 2017 International Conference on Cyberworlds, CW 2017 - in cooperation with: Eurographics Association International Federation for Information Processing ACM SIGGRAPH

Volume: 2017-January

Pages: 41-47

ISBN: 9781538620892

DOI: 10.1109/CW.2017.44

© 2017 IEEE. Markerless Augmented Reality (AR) registration using the standard Homography matrix is unstable, and for image-based registration it has very low accuracy. In this paper, we present a new method to improve the stability and the accuracy of marker-less registration in AR. Based on the Visual Simultaneous Localization and Mapping (V-SLAM) framework, our method adds a three-dimensional dense cloud processing step to the state-of-the-art ORB-SLAM in order to deal with mainly the point cloud fusion and the object recognition. Our algorithm for the object recognition process acts as a stabilizer to improve the registration accuracy during the model to the scene transformation process. This has been achieved by integrating the Hough voting algorithm with the Iterative Closest Points(ICP) method. Our proposed AR framework also further increases the registration accuracy with the use of integrated camera poses on the registration of virtual objects. Our experiments show that the proposed method not only accelerates the speed of camera tracking with a standard SLAM system, but also effectively identifies objects and improves the stability of markerless augmented reality applications.

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