Skip to main content
Springer Nature Link
Log in

Global-Map-Registered Local Visual Odometry Using On-the-Fly Pose Graph Updates

  • Conference paper
  • First Online:
Augmented Reality, Virtual Reality, and Computer Graphics (AVR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12242))

  • 2517 Accesses

Abstract

Real-time camera pose estimation is one of the indispensable technologies for Augmented Reality (AR). While a large body of work in Visual Odometry (VO) has been proposed for AR, practical challenges such as scale ambiguities and accumulative errors still remain especially when we apply VO to large-scale scenes due to limited hardware and resources. We propose a camera pose registration method, where a local VO is consecutively optimized with respect to a large-scale scene map on the fly. This framework enables the scale estimation between a VO map and a scene map and reduces accumulative errors by finding corresponding locations in the map to the current frame and by on-the-fly pose graph optimization. The results using public datasets demonstrated that our approach reduces the accumulative errors of naïve VO.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Burri, M., et al.: The EuRoC micro aerial vehicle datasets. Int. J. Robot. Res. 35, 1157–1163 (2016)

    Article  Google Scholar 

  2. Castle, R., Klein, G., Murray, D.W.: Video-rate localization in multiple maps for wearable augmented reality. In: International Symposium on Wearable Computers, pp. 15–22 (2008)

    Google Scholar 

  3. Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40, 611–625 (2017)

    Article  Google Scholar 

  4. Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 834–849. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10605-2_54

    Chapter  Google Scholar 

  5. Forster, C., Pizzoli, M., Scaramuzza, D.: SVO: fast semi-direct monocular visual odometry. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 15–22. IEEE (2014)

    Google Scholar 

  6. Gálvez-López, D., Tardos, J.D.: Bags of binary words for fast place recognition in image sequences. IEEE Trans. Robot. 28, 1188–1197 (2012)

    Article  Google Scholar 

  7. Gao, X., Wang, R., Demmel, N., Cremers, D.: LDSO: direct sparse odometry with loop closure. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2198–2204. IEEE (2018)

    Google Scholar 

  8. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354–3361. IEEE (2012)

    Google Scholar 

  9. Kume, H., Suppé, A., Kanade, T.: Vehicle localization along a previously driven route using an image database. In: IAPR International Conference on Machine Vision Applications (MVA), pp. 177–180 (2013)

    Google Scholar 

  10. Kümmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: g2o: a general framework for graph optimization. In: 2011 IEEE International Conference on Robotics and Automation, pp. 3607–3613. IEEE (2011)

    Google Scholar 

  11. Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33, 1255–1262 (2017)

    Article  Google Scholar 

  12. Qin, T., Li, P., Shen, S.: Relocalization, global optimization and map merging for monocular visual-inertial SLAM. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1197–1204. IEEE (2018)

    Google Scholar 

  13. Qin, T., Li, P., Shen, S.: VINS-Mono: a robust and versatile monocular visual-inertial state estimator. IEEE Trans. Robot. 34, 1004–1020 (2018)

    Article  Google Scholar 

  14. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.R.: ORB: an efficient alternative to SIFT or SURF. In: International Conference on Computer Vision (ICCV), pp. 2564–2571. IEEE (2011)

    Google Scholar 

  15. Schneider, T., et al.: Maplab: an open framework for research in visual-inertial mapping and localization. IEEE Robot. Autom. Lett. 3, 1418–1425 (2018)

    Article  Google Scholar 

  16. Schönberger, J.L., Zheng, E., Frahm, J.-M., Pollefeys, M.: Pixelwise view selection for unstructured multi-view stereo. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 501–518. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_31

    Chapter  Google Scholar 

  17. Schönberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4104–4113. IEEE (2016)

    Google Scholar 

  18. Taketomi, T., Sato, T., Yokoya, N.: Real-time and accurate extrinsic camera parameter estimation using feature landmark database for augmented reality. Comput. Graph. 35, 768–777 (2011)

    Article  Google Scholar 

  19. Taketomi, T., Uchiyama, H., Ikeda, S.: Visual SLAM algorithms: a survey from 2010 to 2016. IPSJ Trans. Comput. Vis. Appl. 9(1), 1–11 (2017). https://doi.org/10.1186/s41074-017-0027-2

    Article  Google Scholar 

  20. Umeyama, S.: Least-squares estimation of transformation parameters between two point patterns. IEEE Trans. Pattern Anal. Mach. Intell. 13(4), 376–380 (1991)

    Article  Google Scholar 

  21. Ventura, J., Arth, C., Reitmayr, G., Schmalstieg, D.: Global localization from monocular SLAM on a mobile phone. IEEE Trans. Visual. Comput. Graph. 20, 531–539 (2014)

    Article  Google Scholar 

  22. Ventura, J., Höllerer, T.: Wide-area scene mapping for mobile visual tracking. In: Int. Symp. on Mixed and Augmented Reality (ISMAR), pp. 3–12 (2012)

    Google Scholar 

  23. Von Stumberg, L., Usenko, V., Cremers, D.: Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2510–2517. IEEE (2018)

    Google Scholar 

  24. Wang, R., Schworer, M., Cremers, D.: Stereo DSO: large-scale direct sparse visual odometry with stereo cameras. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3903–3911 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information and Computer Science, Keio University, Yokohama, 223-8522, Japan

    Masahiro Yamaguchi & Hideo Saito

  2. Institute of Computer Graphics and Vision, Graz University of Technology, 8010, Graz, Austria

    Shohei Mori

  3. NEC, Kawasaki, 211–8666, Japan

    Shoji Yachida & Takashi Shibata

Authors
  1. Masahiro Yamaguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shohei Mori
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hideo Saito
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shoji Yachida
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Takashi Shibata
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Masahiro Yamaguchi .

Editor information

Editors and Affiliations

  1. University of Salento, Lecce, Italy

    Lucio Tommaso De Paolis

  2. University of Paris-Sud, Orsay, France

    Patrick Bourdot

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yamaguchi, M., Mori, S., Saito, H., Yachida, S., Shibata, T. (2020). Global-Map-Registered Local Visual Odometry Using On-the-Fly Pose Graph Updates. In: De Paolis, L., Bourdot, P. (eds) Augmented Reality, Virtual Reality, and Computer Graphics. AVR 2020. Lecture Notes in Computer Science(), vol 12242. Springer, Cham. https://doi.org/10.1007/978-3-030-58465-8_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58465-8_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58464-1

  • Online ISBN: 978-3-030-58465-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics