skip to main content
10.1145/2671015.2671115acmconferencesArticle/Chapter ViewAbstractPublication PagesvrstConference Proceedingsconference-collections
research-article

Robust 6-DOF immersive navigation using commodity hardware

Published:11 November 2014Publication History

ABSTRACT

In this paper we present a novel visual-inertial 6-DOF localization approach that can be directly integrated in a wearable immersive system for simulation and training. In this context, while CAVE environments typically require complex and expensive set-up, our approach relies on visual and inertial information provided by commodity hardware, i.e. a consumer monocular camera and an Inertial Measurement Unit (IMU).

We propose a novel robust pipeline based on state-of-the-art image-based localization and sensor fusion approaches. A loosely-coupled sensor fusion approach, which makes use of robust orientation information from the IMU, is employed to cope with failures in visual tracking (e.g. due to camera fast motion) in order to limit motion jitters. Fast and smooth re-localization is also provided to track position following visual tracking outage and guarantee continued operation. The 6-DOF information is then used to render consistently VR contents on a stereoscopic HMD. The proposed system, demonstrated in the context of Construction, runs at 30 fps on a standard PC and requires a very limited set-up for its intended application.

Skip Supplemental Material Section

Supplemental Material

References

  1. Aron, M., Simon, G., and Berger, M.-O. 2007. Use of inertial sensors to support video tracking. Comput. Animat. Virtual Worlds 18, 1 (Feb.), 57--68. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Bleser, G., and Stricker, D. 2008. Advanced tracking through efficient image processing and visual-inertial sensor fusion. In IEEE VR '08, 137--144.Google ScholarGoogle Scholar
  3. Chen, W., Plancoulaine, A., Férey, N., Touraine, D., Nelson, J., and Bourdot, P. 2013. 6DoF Navigation in Virtual Worlds: Comparison of joystick-based and head-controlled paradigms. In ACM VRST '13, 111--114. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Cruz-Neira, C., Sandin, D. J., DeFanti, T. A., Kenyon, R. V., and C. Hart, J. 1992. The CAVE: Audio visual experience automatic virtual environment. Commun. ACM 35, 6 (June), 64--72. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Gauglitz, S., Höllerer, T., and Turk, M. 2011. Evaluation of interest point detectors and feature descriptors for visual tracking. Int. J. Comput. Vis. 94, 3, 335--360. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Haralick, B., Lee, C.-N., Ottenberg, K., and Nolle, M. 1994. Review and analysis of solutions of the three point perspective pose estimation problem. Int. J. Comput. Vis. 13, 3, 331--356. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Hay, S., Newman, J., and Harle, R. 2008. Optical tracking using commodity hardware. In ISMAR 2008, 159--160. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Heinly, J., Dunn, E., and Frahm, J.-M. 2012. Comparative evaluation of binary features. In ECCV 2012. 759--773. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Lim, H., Sinha, S. N., Cohen, M. F., and Uyttendaele, M. 2012. Real-time image-based 6-DOF localization in large-scale environments. In IEEE CVPR '12, 1043--1050. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Oskiper, T., Chiu, H.-P., Zhu, Z., Samaresekera, S., and Kumar, R. 2011. Stable vision-aided navigation for large-area augmented reality. In IEEE VR 2011, 63--70. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Perea, L., How, J., Breger, L., and Elosegui, P. 2007. Nonlinearity in sensor fusion: Divergence issues in EKF, modified truncated SOF, and UKF. In Proc. AIAA Guidance, Navigation, and Control Conf. 2007.Google ScholarGoogle ScholarCross RefCross Ref
  12. Shi, J., and Tomasi, C. 1994. Good features to track. In Proceedings CVPR '94, 593--600.Google ScholarGoogle Scholar
  13. Snavely, N., Seitz, S. M., and Szeliski, R. 2008. Modeling the world from internet photo collections. Int. J. Comput. Vision 80, 2 (Nov.), 189--210. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Welch, G., and Foxlin, E. 2002. Motion tracking: No silver bullet, but a respectable arsenal. IEEE Comput. Graph. Appl. 22, 6 (Nov.), 24--38. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Williams, B., Narasimham, G., Rump, B., McNamara, T. P., Carr, T. H., Rieser, J., and Bodenheimer, B. 2007. Exploring large virtual environments with an HMD when physical space is limited. In Proc. 4th APGV Symposium, 41--48. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Zhu, Z., Oskiper, T., Samarasekera, S., Kumar, R., and Sawhney, H. 2008. Real-time global localization with a pre-built visual landmark database. In IEEE CVPR 2008, 1--8.Google ScholarGoogle Scholar

Index Terms

  1. Robust 6-DOF immersive navigation using commodity hardware

        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 Conferences
          VRST '14: Proceedings of the 20th ACM Symposium on Virtual Reality Software and Technology
          November 2014
          238 pages
          ISBN:9781450332538
          DOI:10.1145/2671015

          Copyright © 2014 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: 11 November 2014

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Acceptance Rates

          Overall Acceptance Rate66of254submissions,26%

          Upcoming Conference

          VRST '24

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader