Skip to main content

MOARSLAM: Multiple Operator Augmented RSLAM

  • Conference paper
  • First Online:

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 112 ))

Abstract

To effectively act on the same physical space, robots must first communicate to share and fuse the map of the area in which they operate. For long-term online operation, the merging of maps from heterogeneous devices must be fast and allow for scalable growth in both the number of clients and the size of the map. This paper presents a system which allows multiple clients to share and merge maps built from a state-of-the-art relative SLAM system. Maps can also be augmented with virtual elements that are consistently shared by all the clients. The visual-inertial mapping framework which underlies this system is discussed, along with the server architecture and novel integrated multi-session loop closure system. We show quantitative results of the system. The map fusion benefits are demonstrated with an example augmented reality application.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Aragues, R., Cortes, J., Sagues, C.: Distributed consensus on robot networks for dynamically merging feature-based maps. IEEE Trans. Robot. 28(4), 840–854 (2012)

    Article  Google Scholar 

  2. ARPG. Node. https://github.com/arpg/Node

  3. Bryson, M., Sukkarieh, S.: Architectures for cooperative airborne simultaneous localisation and mapping. J. Intell. Robot. Syst. 55(4–5), 267–297 (2009)

    Article  MATH  Google Scholar 

  4. Castle, R.O., Klein, G., Murray, D.W.: Wide-area augmented reality using camera tracking and mapping in multiple regions. Comput. Vision Image Underst. 115(6), 854–867 (2011)

    Article  Google Scholar 

  5. Chang, H.J., Lee, C.S.G., Hu, Y.C., Yung-Hsiang, Lu.: Multi-robot SLAM with topological/metric maps. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1467–1472 (2007)

    Google Scholar 

  6. Civera, J., Davison, A.J., Montiel, J.M.M.: Inverse depth parametrization for monocular SLAM. IEEE Trans. Robot. 24(5), 932–945 (2008)

    Article  Google Scholar 

  7. Forster, C., Lynen, S., Kneip, L., Scaramuzza, D.: Collaborative monocular SLAM with multiple micro aerial vehicles. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3962–3970. IEEE (2013)

    Google Scholar 

  8. Gálvez-López, D., Tardós, J.D.: Bags of binary words for fast place recognition in image sequences. IEEE Trans. Robot. 28(5), 1188–1197 (2012)

    Article  Google Scholar 

  9. Keivan, N., Patron-Perez, A., Sibley, G.: Adaptive asynchronous conditioning for visual-inertial SLAM. In: International Symposium on Experimental Robotics, June 2014

    Google Scholar 

  10. Klein, G., Murray, D,: Parallel tracking and mapping for small AR workspaces. In: 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, pp. 225–234 (2007)

    Google Scholar 

  11. Latif, Y., Cadena, C., Neira, J.: Robust loop closing over time for pose graph SLAM. Int. J. Robot. Res. 32(14), 1611–1626 (2013)

    Article  Google Scholar 

  12. Leung, K.Y.K., Barfoot, T.D., Liu, H.H.T.: Distributed and decentralized cooperative simultaneous localization and mapping for dynamic and sparse robot networks. In: IEEE International Conference onRobotics and Automatio, pp. 3841–3847, May 2011

    Google Scholar 

  13. McDonald, J., Kaess, M., Cadena, C., Neira, J., Leonard, J.J.: Real-time 6-DOF multi-session visual SLAM over large-scale environments. Robot. Auton. Syst. 61(10), 1144–1158 (2013)

    Article  Google Scholar 

  14. Mei, C., Sibley, G., Cummins, M., Newman, P., Reid, I.: RSLAM: a system for large-scale mapping in constant-time using stereo. Int. J. Comput. Vision 94(2), 198–214 (2011)

    Article  Google Scholar 

  15. Olson, E., Agarwal, P.: Inference on networks of mixtures for robust robot mapping. Int. J. Robot. Res. 32(7), 826–840 (2013)

    Article  Google Scholar 

  16. Ergin Özkucur, N., Levent Akin, H.: Cooperative multi-robot map merging using fast-SLAM. In: RoboCup 2009: Robot Soccer World Cup XIII, number 5949 in Lecture Notes in Computer Science, pp. 449–460, Jan 2010

    Google Scholar 

  17. Riazuelo, L., Civera, J., Montiel, J.M.M.: C2TAM: a cloud framework for cooperative tracking and mapping. Robot. Auton. Syst. 62(4), 401–413 (2014)

    Article  Google Scholar 

  18. Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: European Conference on Computer Vision, pp. 430–443 (2006)

    Google Scholar 

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

    Google Scholar 

  20. Salas-Moreno, R.F., Newcombe, R.A., Strasdat, H., Kelly, P.H.J., Davison, A.J.: SLAM++: simultaneous localisation and mapping at the level of objects. In: IEEE Computer Vision and Pattern Recognition, June 2013

    Google Scholar 

  21. Sharma, R., Taylor, C., Casbeer, D.W., Beard, R.W.: Distributed cooperative slam using an information consenseus filter. In: AIAA Guidance Navigation and Control Conference, pp. 8334–8342 (2010)

    Google Scholar 

  22. Waibel, M., Beetz, M., Civera, J., D’Andrea, R., Elfring, J., Gálvez-López, D., Haussermann, K., Janssen, R., Montiel, J.M.M., Perzylo, A., Schiessle, B., Tenorth, M., Zweigle, O., van de Molengraft, R.: Roboearth. IEEE Robot. Autom. Mag. 18(2), 69–82 (2011)

    Google Scholar 

Download references

Acknowledgments

This work is made possible with generous support from Google Project Tango.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to John G. Morrison .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Japan

About this paper

Cite this paper

Morrison, J.G., Gálvez-López, D., Sibley, G. (2016). MOARSLAM: Multiple Operator Augmented RSLAM. In: Chong, NY., Cho, YJ. (eds) Distributed Autonomous Robotic Systems. Springer Tracts in Advanced Robotics, vol 112 . Springer, Tokyo. https://doi.org/10.1007/978-4-431-55879-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-4-431-55879-8_9

  • Published:

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-55877-4

  • Online ISBN: 978-4-431-55879-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics