Skip to main content
SpringerLink
Log in
Book cover

International Conference on Robot Intelligence Technology and Applications

RiTA 2017: Robot Intelligence Technology and Applications 5 pp 165–183Cite as

Multi-robot SLAM: An Overview and Quantitative Evaluation of MRGS ROS Framework for MR-SLAM

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 751))

Abstract

In recent years, multi-robot systems (MRS) have received attention from researchers in academia, government laboratories and industry. This research activity has borne fruit in tackling some of the challenging problems that are still open. One is multi-robot simultaneous localization and mapping (MR-SLAM). This paper provides an overview of the latest trends in tackling the problem of (MR-SLAM) focusing on Robot Operating System (ROS) enabled package designed to solve this problem through enabling the robots to share their maps and merge them over a WiFi network. This package had some out-of-date dependencies and worked with some packages that no longer exist. The package has been modified to handle these dependencies. The C-SLAM package was then tested with 2 robots using Gmapping and Hector SLAM packages. Quantitative metrics were used to evaluate the accuracy of the generated maps by comparing it to the ground truth map, including Map Score and Occupied/Free cells ratio.

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

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    http://wiki.ros.org/gmapping.

References

  1. Andersson, L.A.A., Nygards, J.: On multi-robot map fusion by inter-robot observations. In: 12th International Conference on Information Fusion, FUSION’09, pp. 1712–1721 (2009)

    Google Scholar 

  2. Bresson, G., Aufrre, R., Chapuis, R.: Consistent multi-robot decentralized slam with unknown initial positions. In: 16th International Conference on Information Fusion (FUSION), pp. 372–379 (2013)

    Google Scholar 

  3. Carpin, S.: Fast and accurate map merging for multi-robot systems. Auton. Robot. 25(3), 305–316 (2008). https://doi.org/10.1007/s10514-008-9097-4

    Article  Google Scholar 

  4. Chang, C.K., Chang, C.H., Wang, C.C.: Communication adaptive multi-robot simultaneous localization and tracking via hybrid measurement and belief sharing. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 5016–5023 (2014). https://doi.org/10.1109/ICRA.2014.6907594

  5. Endsley, M.: Toward a theory of situation awareness in dynamic systems. Hum. Factors 37(1), 32–64 (1995). https://doi.org/10.1518/001872095779049543

    Article  Google Scholar 

  6. Fernandez-Madrigal, J.-A., Blanco Claraco, J.L.: Simultaneous Localization and Mapping for Mobile Robots, 1st edn. Information Science Reference (2013)

    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 (2013). https://doi.org/10.1109/IROS.2013.6696923

  8. Fox, D., Ko, J., Konolige, K., Limketkai, B., Schulz, D., Stewart, B.: Distributed multirobot exploration and mapping. Proc. IEEE 94(7), 1325–1339 (2006). https://doi.org/10.1109/JPROC.2006.876927

    Article  Google Scholar 

  9. Gong, C., Tully, S., Kantor, G., Choset, H.: Multi-agent deterministic graph mapping via robot rendezvous. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1278–1283 (2012). https://doi.org/10.1109/ICRA.2012.6225274

  10. Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Trans. Robot. 23(1), 34–46 (2007). https://doi.org/10.1109/TRO.2006.889486

    Article  Google Scholar 

  11. Howard, A., Matark, M.J., Sukhatme, G.S.: Localization for mobile robot teams using maximum likelihood estimation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, pp. 434–439 (2002). https://doi.org/10.1109/IRDS.2002.1041428

  12. Jacques, P., Deneubourg, J.L.: From Individual to Collective Behavior in Social Insects, 1st edn. Birkhäuser Verlag, Basel (1987)

    Google Scholar 

  13. Kaess, M., Ranganathan, A., Dellaert, F.: Isam: incremental smoothing and mapping. IEEE Trans. Robot. 24(6), 1365–1378 (2008). https://doi.org/10.1109/TRO.2008.2006706

    Article  Google Scholar 

  14. Karam, N., Chausse, F., Aufrere, R., Chapuis, R.: Localization of a group of communicating vehicles by state exchange. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 519–524 (2006). https://doi.org/10.1109/IROS.2006.282028

  15. Lee, H.C., Roh, B.S., Lee, B.H.: Multi-hypothesis map merging with sinogram-based pso for multi-robot systems. Electron. Lett. 52(14), 1213–1214 (2016). https://doi.org/10.1049/el.2016.1041

    Article  Google Scholar 

  16. Lee, S., Cho, H., Yoon, K.J., Lee, J.: Mapping of Incremental Dynamic Environment Using Rao-Blackwellized Particle Filter, pp. 715–724. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-33926-4_68

  17. 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 on Robotics and Automation (ICRA), pp. 3841–3847 (2011). https://doi.org/10.1109/ICRA.2011.5979783

  18. Li, Q.L., Song, Y., Hou, Z.G.: Neural network based fastslam for autonomous robots in unknown environments. Neurocomputing 165, 99–110 (2015). https://doi.org/10.1016/j.neucom.2014.06.095, http://www.sciencedirect.com/science/article/pii/S0925231215004312

    Article  Google Scholar 

  19. Li, Z., Riaz, S., Chellali, R.: Visual place recognition for multi-robots maps merging, pp. 1–6. IEEE (2012)

    Google Scholar 

  20. Miller, I., Campbell, M.: Rao-blackwellized particle filtering for mapping dynamic environments. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3862–3869 (2007). https://doi.org/10.1109/ROBOT.2007.364071

  21. Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: Fastslam: A factored solution to the simultaneous localization and mapping problem. In: Eighteenth National Conference on Artificial Intelligence, pp. 593–598. American Association for Artificial Intelligence, Menlo Park, CA, USA (2002). http://dl.acm.org/citation.cfm?id=777092.777184

  22. Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: Fastslam 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1151–1156 (2003)

    Google Scholar 

  23. Moravec, H., Elfes, A.: High resolution maps from wide angle sonar. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 2, pp. 116–121 (1985). https://doi.org/10.1109/ROBOT.1985.1087316

  24. Moravec, M.: Robot Evidence grids (1996)

    Google Scholar 

  25. un Nabi Jafri, S.R., Ahmed, W., Ashraf, Z., Chellali, R.: Multi robot slam for features based environment modelling. In: IEEE International Conference on Mechatronics and Automation, pp. 711–716 (2014). https://doi.org/10.1109/ICMA.2014.6885784

  26. un Nabi Jafri, S.R., Li, Z., Chandio, A.A., Chellali, R.: Laser only feature based multi robot slam. In: 12th International Conference on Control Automation Robotics Vision (ICARCV), pp. 1012–1017 (2012). https://doi.org/10.1109/ICARCV.2012.6485296

  27. Nerurkar, E.D., Roumeliotis, S.I., Martinelli, A.: Distributed maximum a posteriori estimation for multi-robot cooperative localization. In: IEEE International Conference on Robotics and Automation, ICRA’09, pp. 1402–1409 (2009). https://doi.org/10.1109/ROBOT.2009.5152398

  28. OSullivan, S.: An empirical evaluation of map building methodologies in mobile robotics using the feature prediction sonar noise filter and metric grid map benchmarking suite. Master’s thesis, University of Limerick (2003)

    Google Scholar 

  29. Park, J., Sinclair, A.J., Sherrill, R.E., Doucette, E.A., Curtis, J.W.: Map merging of rotated, corrupted, and different scale maps using rectangular features. In: IEEE/ION Position, Location and Navigation Symposium (PLANS), pp. 535–543 (2016). https://doi.org/10.1109/PLANS.2016.7479743

  30. Pedro, J.: Smokenav simultaneous localization and mapping in reduced visibility scenarios. Master’s thesis, University of Coimbra (2013)

    Google Scholar 

  31. Romero, V.A., Costa, O.L.V.: Map merging strategies for multi-robot fastslam: a comparative survey. In: Latin American Robotics Symposium and Intelligent Robotic Meeting (LARS), pp. 61–66 (2010). https://doi.org/10.1109/LARS.2010.20

  32. Roumeliotis, S.I., Bekey, G.: Distributed multirobot localization. IEEE Trans. Robot. Autom. 18(5), 781–795 (2002). https://doi.org/10.1109/tra.2002.803461

    Article  Google Scholar 

  33. Rybski, P.E., Roumeliotis, S.I., Gini, M., Papanikolopoulos, N.: A comparison of maximum likelihood methods for appearance-based minimalistic slam. In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA’04, vol. 2, pp. 1777–1782 (2004).https://doi.org/10.1109/ROBOT.2004.1308081

  34. Saeedi, S., Paull, L., Trentini, M., Li, H.: Neural network-based multiple robot simultaneous localization and mapping. IEEE Trans. Neural Netw. 22(12), 2376–2387 (2011). https://doi.org/10.1109/TNN.2011.2176541

    Article  Google Scholar 

  35. Saeedi, S., Trentini, M., Seto, M., Li, H.: Multiple-robot simultaneous localization and mapping: a review. J. Field Robot. 33(1), 3–46 (2015). https://doi.org/10.1002/rob.21620

    Article  Google Scholar 

  36. dos Santos, G.: A cooperative slam framework with efficient information sharing over mobile ad hoc networks. Master’s thesis, University of Coimbra (2014)

    Google Scholar 

  37. Shoudong Huang, G.D.: Convergence and consistency analysis for extended kalman filter based slam. IEEE Trans. Robot. 23(5), 1036–1049 (2007). https://doi.org/10.1109/tro.2007.903811

    Article  Google Scholar 

  38. Stipes, J., Hawthorne, R., Scheidt, D., Pacifico, D.: Cooperative localization and mapping. In: IEEE International Conference on Networking, Sensing and Control, pp. 596–601 (2006). https://doi.org/10.1109/ICNSC.2006.1673213

  39. Swinnerton, J., Brimble, R.: Autonomous self-localization and mapping agents. In: 7th International Conference on Information Fusion, vol. 2 (2005), pp. 7. https://doi.org/10.1109/ICIF.2005.1591990

  40. Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics, 1st edn. MIT press, Cambridge (2005)

    MATH  Google Scholar 

  41. Zhou, X.S., Roumeliotis, S.I.: Multi-robot slam with unknown initial correspondence: The robot rendezvous case. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1785–1792 (2006). https://doi.org/10.1109/IROS.2006.282219

Download references

Author information

Authors and Affiliations

  1. Aerospace Engineering, Zewail City of Science and Technology, Giza, Egypt

    Mahmoud A. Abdulgalil, Mahmoud M. Nasr & Mohamed H. Elalfy

  2. Centre for Pattern Analysis and Machine Intelligence (CPAMI), University of Waterloo, Waterloo, ON, Canada

    Alaa Khamis & Fakhri Karray

Authors
  1. Mahmoud A. Abdulgalil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahmoud M. Nasr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed H. Elalfy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alaa Khamis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fakhri Karray
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alaa Khamis .

Editor information

Editors and Affiliations

  1. School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea (Republic of)

    Jong-Hwan Kim

  2. Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea (Republic of)

    Hyun Myung

  3. School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea (Republic of)

    Junmo Kim

  4. Department of Mechanical Engineering, The University of Auckland, Auckland, New Zealand

    Weiliang Xu

  5. Department of Computer and Information Technology, Purdue University, West Lafayette, Indiana, USA

    Eric T Matson

  6. Department of Computer Science and Engineering, Dongguk University, Seoul, Korea (Republic of)

    Jin-Woo Jung

  7. Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea (Republic of)

    Han-Lim Choi

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Abdulgalil, M.A., Nasr, M.M., Elalfy, M.H., Khamis, A., Karray, F. (2019). Multi-robot SLAM: An Overview and Quantitative Evaluation of MRGS ROS Framework for MR-SLAM. In: Kim, JH., et al. Robot Intelligence Technology and Applications 5. RiTA 2017. Advances in Intelligent Systems and Computing, vol 751. Springer, Cham. https://doi.org/10.1007/978-3-319-78452-6_15

Download citation

Publish with us

Policies and ethics

Search

Navigation