skip to main content
10.1145/2948628.2948632acmotherconferencesArticle/Chapter ViewAbstractPublication PagessccgConference Proceedingsconference-collections
research-article

Visual correction of position drift using uniform marker fields

Published:27 April 2016Publication History

ABSTRACT

This paper deals with navigation indoors and in small-scale outdoor scenes. We propose to enhance IMU and conventional visual odometry navigation - typically used in the said contexts - by visual location estimation based on markers conveniently displaced in the environment. We propose using visually appealing Uniform Marker Fields; this technique offers high precision and real-time performance on mobile devices of moderate computational power. We evaluate the accuracy of the proposed technique, achieving approximately 4cm error in a scene of roughly 5 m diameter (beneath 1 %).

References

  1. Assa, A., and Janabi-Sharifi, F. 2015. A kalman filter-based framework for enhanced sensor fusion. Sensors Journal, IEEE.Google ScholarGoogle Scholar
  2. Burns, J., and Mitchell, C. J. 1993. Coding schemes for two-dimensional position sensing. Institute of Mathematics and Its Applications Conference Series 45, 31.Google ScholarGoogle Scholar
  3. Chiu, H.-P., Williams, S., Dellaert, F., Samarasekera, S., and Kumar, R. 2013. Robust vision-aided navigation using sliding-window factor graphs. In Robotics and Automation (ICRA), 2013 IEEE International Conference on.Google ScholarGoogle Scholar
  4. Fiala, M. 2005. ARTag, a fiducial marker system using digital techniques. In Proc. CVPR. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Fiala, M. 2010. Designing highly reliable fiducial markers. IEEE PAMI 32, 7 (July), 1317--1324. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Herout, A., Szentandrási, I., Zachariáš, M., Dubská, M., and Kajan, R. 2013. Five shades of grey for fast and reliable camera pose estimation. In Proceedings of CVPR, IEEE Computer Society, 1384--1390. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Hirzer, M. 2008. Marker detection for augmented reality applications. Tech. rep., Inst. for Comp. Graphics and Vision, Graz Univ. of Tech., AT.Google ScholarGoogle Scholar
  8. Kato, H., and Billinghurst, M. 1999. Marker tracking and HMD calibration for a video-based ar conferencing system. In Proc. IWAR. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Klein, G., and Murray, D. 2007. Parallel tracking and mapping for small AR workspaces. In ISMAR. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Leutenegger, S., Melzer, A., Alexis, K., and Siegwart, R. 2014. Robust state estimation for small unmanned airplanes. In Control Applications (CCA), 2014 IEEE Conference on, 1003--1010.Google ScholarGoogle Scholar
  11. Oskiper, T., Samarasekera, S., and Kumar, R. 2012. Multi-sensor navigation algorithm using monocular camera, IMU and GPS for large scale augmented reality. In 11th IEEE International Symposium on Mixed and Augmented Reality, IS-MAR 2012, Atlanta, GA, USA, November 5-8, 2012, IEEE Computer Society. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Paces, P., and Suchy, J. 2014. Statistical evaluation of multiple low-cost mems sensors for altitude measurement. In Digital Avionics Systems Conference (DASC), 2014 IEEE/AIAA 33rd.Google ScholarGoogle Scholar
  13. Schaffalitzky, F., and Zisserman, A. 2000. Planar grouping for automatic detection of vanishing lines and points. Image and Vision Computing 18, 647--658.Google ScholarGoogle ScholarCross RefCross Ref
  14. Szentandrási, I., Zachariáš, M., Havel, J., Herout, A., Dubská, M., and Kajan, R. 2012. Uniform Marker Fields: Camera loc. by orientable De Bruijn tori. In ISMAR.Google ScholarGoogle Scholar
  15. Wald, A. 1945. Sequential tests of statistical hypotheses. The Annals of Mathematical Statistics 16, 2, 117--186.Google ScholarGoogle ScholarCross RefCross Ref
  16. Zhang, Z. 2000. A flexible new technique for camera calibration. Pattern Analysis and Machine Intelligence, IEEE Transactions on 22, 11 (Nov), 1330--1334. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Zhao, H., and Wang, Z. 2012. Motion measurement using inertial sensors, ultrasonic sensors, and magnetometers with extended kalman filter for data fusion. Sensors Journal, IEEE.Google ScholarGoogle Scholar
  18. Zhu, Z., Oskiper, T., Samarasekera, S., Kumar, R., and Sawhney, H. 2007. Ten-fold improvement in visual odometry using landmark matching. In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on.Google ScholarGoogle Scholar

Index Terms

  1. Visual correction of position drift using uniform marker fields

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        SCCG '16: Proceedings of the 32nd Spring Conference on Computer Graphics
        April 2016
        89 pages
        ISBN:9781450344364
        DOI:10.1145/2948628

        Copyright © 2016 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 27 April 2016

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate42of81submissions,52%
      • Article Metrics

        • Downloads (Last 12 months)1
        • Downloads (Last 6 weeks)0

        Other Metrics

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader