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A Method of Registering Virtual Objects in Monocular Augmented Reality System

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Book cover Image and Graphics Technologies and Applications (IGTA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 875))

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Abstract

A flexible novel method of registering virtual objects in monocular AR system is presented in this paper. Monocular AR systems use SLAM-related techniques to obtain the camera pose, of which the translation component is on a random scale. We add a scale calibration process to acquire the scale factor from the SLAM map to the real world and provide a closed-form solution of the transformation between two coordinate systems with different scales. We also describe the framework of an AR system based on our method with implementation. The proposed system can easily initialize virtual objects’ position, orientation and size by using a known reference in the real scene and the reference is no longer needed in the later process. Our method is flexible, simple to set up and easy to control. The results show the proposed method can apply to real-time interactive AR applications.

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References

  1. Kaufmann, H.: Construct3D: an augmented reality application for mathematics and geometry education. In: ACM Multimedia, pp. 656–657. (2002)

    Google Scholar 

  2. Bichlmeier, C., Sielhorst, T., Heining, S.M., Navab, N.: Improving depth perception in medical AR. In: Horsch, A., Deserno, T.M., Handels, H., Meinzer, H.P., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin 2007. Informatik aktuell, pp. 217–221. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71091-2_44

    Chapter  Google Scholar 

  3. Livingston, M.A., et al.: Military applications of augmented reality. In: Furht, B. (ed.) Handbook of Augmented Reality, pp. 671–706. Springer, New York (2011). https://doi.org/10.1007/978-1-4614-0064-6_31

    Chapter  Google Scholar 

  4. Pucihar, K.C., Coulton, P.: Exploring the evolution of mobile augmented reality for future entertainment systems. Conf. Comput. Eur. 11, 1–16 (2013)

    Article  Google Scholar 

  5. Carvalho, C.V.D.A., Lemos, B.M.: Possibilities of augmented reality use in mathematics aiming at a meaningful learning. Creat. Educ. 05, 690–700 (2014)

    Article  Google Scholar 

  6. Feng, Z., Duh, H.B.L., Billinghurst, M.: Trends in augmented reality tracking, interaction and display: a review of ten years of ISMAR. In: 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality, pp. 193–202 (2008)

    Google Scholar 

  7. Detone, D., Malisiewicz, T., Rabinovich, A.: Toward geometric deep SLAM (2017)

    Google Scholar 

  8. ARKit Hardware and Software Integration. https://developer.apple.com/arkit/

  9. Chekhlov, D., Gee, A.P., Calway, A., Mayol-Cuevas, W.: Ninja on a plane: automatic discovery of physical planes for augmented reality using visual SLAM. In: Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, pp. 1–4. IEEE Computer Society (2007)

    Google Scholar 

  10. Klein, G., Murray, D.W.: Parallel tracking and mapping for small AR workspaces. In: international symposium on mixed and augmented reality, pp. 225–234 (2007)

    Google Scholar 

  11. Liu, H., Zhang, G., Bao, H.: Robust keyframe-based monocular SLAM for augmented reality. In: 2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 1–10 (2016)

    Google Scholar 

  12. Xue, T., Luo, H., Cheng, D., Yuan, Z., Yang, X.: Real-time monocular dense mapping for augmented reality. In: Proceedings of the 2017 ACM on Multimedia Conference, pp. 510–518. ACM, Mountain View (2017)

    Google Scholar 

  13. Davison, A.J., Reid, I.D., Molton, N., Stasse, O.: MonoSLAM: real-time single camera SLAM. IEEE Trans. Pattern Anal. Mach. Intell. 29, 1052–1067 (2007)

    Article  Google Scholar 

  14. Murartal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Rob. 31, 1147–1163 (2015)

    Article  Google Scholar 

  15. Concha, A., Civera, J.: DPPTAM: dense piecewise planar tracking and mapping from a monocular sequence. In: Intelligent Robots and Systems, pp. 5686–5693 (2015)

    Google Scholar 

  16. Gao, Q.H., Wan, T.R., Tang, W., Chen, L., Zhang, K.B.: An improved augmented reality registration method based on visual SLAM. In: Tian, F., Gatzidis, C., El Rhalibi, A., Tang, W., Charles, F. (eds.) Edutainment 2017. LNCS, vol. 10345, pp. 11–19. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65849-0_2

    Chapter  Google Scholar 

  17. Lupton, T., Sukkarieh, S.: Removing scale biases and ambiguity from 6DoF monocular SLAM using inertial. In: International Conference on Robotics and Automation, pp. 3698–3703 (2008)

    Google Scholar 

  18. Kim, O., Kang, D.: A sensor fusion method to solve the scale ambiguity of single image by combining IMU. In: International Conference on Control and Automation, pp. 923–925 (2015)

    Google Scholar 

  19. Nutzi, G., Weiss, S., Scaramuzza, D., Siegwart, R.Y.: Fusion of IMU and vision for absolute scale estimation in monocular SLAM. J. Intell. Rob. Syst. 61, 287–299 (2011)

    Article  Google Scholar 

  20. Fujimoto, S., Hu, Z., Chapuis, R., Aufrere, R.: ORB-SLAM map initialization improvement using depth. In: International Conference on Image Processing, pp. 261–265 (2016)

    Google Scholar 

  21. Scaramuzza, D.: 1-point-RANSAC structure from motion for vehicle-mounted cameras by exploiting non-holonomic constraints. Int. J. Comput. Vis. 95, 74–85 (2011)

    Article  Google Scholar 

  22. Kitt, B., Rehder, J., Chambers, A., Schönbein, M., Lategahn, H., Singh, S.: Monocular visual odometry using a planar road model to solve scale ambiguity, pp. 43–48 (2011)

    Google Scholar 

  23. Song, S., Chandraker, M.: Robust scale estimation in real-time monocular SFM for autonomous driving. In: Computer Vision and Pattern Recognition, pp. 1566–1573 (2014)

    Google Scholar 

  24. Esteban, I., Dorst, L., Dijk, J.: Closed form solution for the scale ambiguity problem in monocular visual odometry. In: Liu, H., Ding, H., Xiong, Z., Zhu, X. (eds.) ICIRA 2010. LNCS (LNAI), vol. 6424, pp. 665–679. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16584-9_64

    Chapter  Google Scholar 

  25. Li, Y., Wang, S., Yang, D., Sun, D.: Metric online monocular SLAM by using a known reference. In: World Congress on Intelligent Control and Automation, pp. 3002–3006 (2016)

    Google Scholar 

  26. Hartley, R.I., Zisserman, A.: Multiple view geometry in computer vision. Kybernetes 30, 1333–1341 (2000)

    MATH  Google Scholar 

  27. Oberkampf, D., Dementhon, D., Davis, L.S.: Iterative pose estimation using coplanar feature points. Comput. Vis. Image Underst. 63, 495–511 (1996)

    Article  Google Scholar 

  28. Matas, J., Chum, O.: Randomized RANSAC with Td, d test. Image Vis. Comput. 22, 837–842 (2004)

    Article  Google Scholar 

  29. Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F.J., Marín-Jiménez, M.J.: Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recogn. 47, 2280–2292 (2014)

    Article  Google Scholar 

  30. Holden, D., Komura, T., Saito, J.: Phase-functioned neural networks for character control. ACM Trans. Graph. 36, 1–13 (2017)

    Article  Google Scholar 

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Acknowledgements

This work is supported by National Natural Science Foundation of China (No. 61471359) and National Key R&D Plan of China (No. 2016YFB1001404).

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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Zeye Wu, Pengrui Wang & Wujun Che

  2. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China

    Zeye Wu

  3. AICFVE of Beijing Film Academy, Beijing, 100088, China

    Wujun Che

Authors
  1. Zeye Wu
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  2. Pengrui Wang
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  3. Wujun Che
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Corresponding author

Correspondence to Wujun Che .

Editor information

Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Yongtian Wang

  2. Beihang University, Beijing, China

    Zhiguo Jiang

  3. Peking University, Beijing, China

    Yuxin Peng

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Wu, Z., Wang, P., Che, W. (2018). A Method of Registering Virtual Objects in Monocular Augmented Reality System. In: Wang, Y., Jiang, Z., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2018. Communications in Computer and Information Science, vol 875. Springer, Singapore. https://doi.org/10.1007/978-981-13-1702-6_48

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  • DOI: https://doi.org/10.1007/978-981-13-1702-6_48

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1701-9

  • Online ISBN: 978-981-13-1702-6

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