Skip to main content
SpringerLink
Log in

DS-PTAM: Distributed Stereo Parallel Tracking and Mapping SLAM System

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

This paper presents DS-PTAM, a distributed architecture for the S-PTAM stereo SLAM system. This architecture is developed on the ROS framework, separating the localization and mapping tasks into two independent ROS nodes. The DS-PTAM system is ideal for mobile robots with low computing power because it allows to run the localization module on-board and the mapping module —which has a higher computational cost— on a remote base station, relieving the load on the on-board processor. The proposed architecture was implemented based on the original S-PTAM monolithic code and then validated through different experiments on public datasets. The results obtained show the feasibility of the proposed distributed architecture, its correct implementation and the benefits of distributing the computational load on several computers.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Bailey, T., Durrant-Whyte, H.: Simultaneous localization and mapping (SLAM): part II. IEEE Robot. Autom. Mag. 13(3), 108–117 (2006). https://doi.org/10.1109/MRA.2006.1678144

    Article  Google Scholar 

  2. Burri, M., Nikolic, J., Gohl, P., Schneider, T., Rehder, J., Omari, S., Achtelik, M.W., Siegwart, R.: The EuRoC micro aerial vehicle datasets. Int. J. Robot. Res. 35(10), 1157–1163 (2016). https://doi.org/10.1177/0278364915620033

    Article  Google Scholar 

  3. Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., Leonard, J.J.: Past, present, and future of simultaneous localization and mapping: Toward the Robust-Perception age. IEEE Trans. Robot. 32(6), 1309–1332 (2016). https://doi.org/10.1109/TRO.2016.2624754

    Article  Google Scholar 

  4. De Croce, M., Pire, T., Bergero, F.: DiseÑo distribuido de un sistema de mapeo y localización basado en visión para un robot móvil. In: Anales De Las IX Jornadas Argentinas De Robótica, pp. 116–121 (2017)

  5. Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part I. IEEE Robot. Autom. Mag. 13(2), 99–110 (2006). https://doi.org/10.1109/MRA.2006.1638022

    Article  Google Scholar 

  6. Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Proceedings of the European Conference on Computer Vision (ECCV), pp. 834–849. Springer International Publishing, Cham (2014). https://doi.org/10.1007/978-3-319-10605-2_54

  7. Engel, J., Stückler, J., Cremers, D.: Large-scale direct slam with stereo cameras. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 1935–1942. IEEE. https://doi.org/10.1109/IROS.2015.7353631 (2015)

  8. Forster, C., Lynen, S., Kneip, L., Scaramuzza, D.: Collaborative monocular slam with multiple micro aerial vehicles. In: IROS, pp. 3962–3970. IEEE. http://dblp.uni-trier.de/db/conf/iros/iros2013.html#ForsterLKS13 (2013)

  9. Forster, C., Zhang, Z., Gassner, M., Werlberger, M., Scaramuzza, D.: SVO: semidirect visual odometry for monocular and multicamera systems. IEEE Trans. Robot. 33(2), 249–265 (2017). https://doi.org/10.1109/TRO.2016.2623335. http://dblp.uni-trier.de/db/journals/trob/trob33.html#ForsterZGWS17

    Article  Google Scholar 

  10. Gamage, R., Tuceryan, M.: An experimental distributed framework for distributed Simultaneous Localization and Mapping. In: Proceedings of the 2016 IEEE International Conference on Electro Information Technology (EIT), pp. 0665–0667. IEEE. https://doi.org/10.1109/EIT.2016.7535318 (2016)

  11. Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: The KITTI dataset. The international journal of robotics research. IJRR 32(11), 1231–1237 (2013). https://doi.org/10.1177/0278364913491297

    Google Scholar 

  12. Jazwinski, A.H.: Stochastic Processes and Filtering Theory. Mathematics in Science and Engineering. Academic Press, New York (1970). http://opac.inria.fr/record=b1078357. UKM

    MATH  Google Scholar 

  13. Kelly, N.: A Guide to Ultrasonic Sensor set up and Testing Instructions, Limitations, and Sample Applications. Application Note. Michigan State University. Available online: http://www.egr.msu.edu/classes/ece480/capstone/fall09/group05/docs/ece480_dt5_application_note_nkelly.pdf (2009)

  14. Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: Proceedings of the IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 1–10. IEEE Computer Society, Washington (2007). https://doi.org/10.1109/ISMAR.2007.4538852

  15. Mahdoui, N., Natalizio, E., Frémont, V.: Multi-UAVs network communication study for distributed visual simultaneous localization and mapping. In: Proceedings of the 2016 International Conference on Computing, Networking and Communications (ICNC), pp. 1–5. IEEE Computer Society. https://doi.org/10.1109/ICCNC.2016.7440564 (2016)

  16. Metropolis, N., Ulam, S.: The monte carlo method. J. Am. Stat. Assoc. 44(247), 335–341 (1949). https://doi.org/10.1080/01621459.1949.10483310. http://www.tandfonline.com/doi/abs/10.1080/01621459.1949.10483310. PMID: 18139350

    Article  MATH  Google Scholar 

  17. Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras. CoRR arXiv:1610.06475 (2016)

  18. Pire, T., Fischer, T., Castro, G., De Cristóforis, P., Civera, J., Jacobo Berlles, J.: S-PTAM: Stereo parallel tracking and mapping. Robot. Auton. Syst. (RAS) 93, 27–42 (2017). https://doi.org/10.1016/j.robot.2017.03.019

    Article  Google Scholar 

  19. Pire, T., Fischer, T., Civera, J., De Cristóforis, P., Berlles, J.J.: Stereo parallel tracking and mapping for robot localization. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1373–1378 (2015)

  20. Quigley, M., Conley, K., Gerkey, B.P., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)

  21. 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). https://doi.org/10.1016/j.robot.2013.11.007. http://www.sciencedirect.com/science/article/pii/S0921889013002248

    Article  Google Scholar 

  22. Strasdat, H., Davison, A.J., Montiel, J.M.M., Konolige, K.: Double window optimisation for constant time visual SLAM. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2352–2359. https://doi.org/10.1109/ICCV.2011.6126517 (2011)

  23. Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle Adjustment — a Modern Synthesis, chap. 21, pp. 298–372. Springer, Berlin (2000). https://doi.org/10.1007/3-540-44480-7_21

    Google Scholar 

  24. Wendel, A., Maurer, M., Graber, G., Pock, T., Bischof, H.: Dense reconstruction on-the-fly. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), CVPR ’12. https://doi.org/10.1109/CVPR.2012.6247833, pp. 1450–1457. IEEE Computer Society, Washington (2012)

  25. Williams, R., Konev, B., Coenen, F.: Multi-Agent Environment Exploration with AR.Drones, pp. 60–71. Lecture Notes in Computer Science. Springer International Publishing, Cham (2014). https://doi.org/10.1007/978-3-319-10401-0_6

    Google Scholar 

  26. Williams, R., Konev, B., Coenen, F.: Scalable distributed collaborative tracking and mapping with Micro Aerial Vehicles. In: Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3092–3097. https://doi.org/10.1109/IROS.2015.7353804 (2015)

  27. Zanuttigh, P., Marin, G., Dal Mutto, C., Dominio, F., Minto, L., Cortelazzo, G.M.: Operating Principles of Structured Light Depth Cameras, pp. 43–79. Springer International Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-30973-6_2

    Google Scholar 

Download references

Acknowledgements

This work is part of the Development of a weed remotion mobile robot project at CIFASIS (CONICET-UNR).

Author information

Authors and Affiliations

  1. FCEIA-UNR, Av. Pellegrini 250 S2000, Rosario, Argentina

    Mauro De Croce

  2. CIFASIS (CONICET-UNR), 27 de Febrero 210 bis S2000EZP, Rosario, Argentina

    Taihú Pire & Federico Bergero

Authors
  1. Mauro De Croce
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Taihú Pire
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Federico Bergero
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Taihú Pire.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

De Croce, M., Pire, T. & Bergero, F. DS-PTAM: Distributed Stereo Parallel Tracking and Mapping SLAM System. J Intell Robot Syst 95, 365–377 (2019). https://doi.org/10.1007/s10846-018-0913-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-018-0913-6

Keywords

Search

Navigation