Skip to main content

A Cloud-Based Visual SLAM Framework for Low-Cost Agents

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 10528)


Constrained by on-board resource, most of the low-cost robots could not autonomously navigate in unknown environments. In the latest years, cloud computing and storage has been developing rapidly, making it possible to offload parts of visual SLAM processing to a server. However, most of the cloud-based vSLAM frameworks are not suitable or fully tested for the applications of poor-equipped agents. In this paper, we describe an online localization service on a novel cloud-based framework, where the expensive map storage and global feature matching are provided as a service to agents. It enables a scenario that only sensor data collection is executed on agents, while the cloud aids the agents to localize and navigate. At the end, we evaluate the localization service quantitatively and qualitatively. The results indicate that the proposed cloud framework can fit the requirement of real-time applications.


  • Cloud robotics
  • Mobile robot
  • Visual SLAM

This work was sponsored by the Research Grant Council of Hong Kong SAR Government, China, under project Nos. 16212815, 21202816 and National Natural Science Foundation of China Nos. 6140021318 and 61640305; Shenzhen Science, Technology and Innovation Comission (SZSTI) JCYJ20160428154842603 and JCYJ20160401100022706; partially supported by the HKUST Project IGN16EG12. All rewarded to Prof. Ming Liu.

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-68345-4_42
  • Chapter length: 14 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-68345-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.


  1. 1.

    OpenResty: a registered trademark owned by OpenResty Inc.

  2. 2.

    OrangePi Plus2:

  3. 3.

    JSON: a lightweight data-interchange that is easy for humans to read and write.

  4. 4.

    RapidJSON: a fast JSON parser/generator for C++.


  1. Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. (2017)

    Google Scholar 

  2. 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). doi:10.1007/978-3-319-10605-2_54

    Google Scholar 

  3. Mur-Artal, R., Tardos, J.D.: ORB-SLAM2: an open-source SLAM system for monocular, stereo and RGB-D cameras. arXiv preprint arXiv:1610.06475 (2016)

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

    CrossRef  Google Scholar 

  5. Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I.D., Leonard, J.J.: Simultaneous localization and mapping: present, future, and the robust-perception age. arXiv preprint arXiv:1606.05830 (2016)

  6. Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular slam system. IEEE Trans. Rob. 31(5), 1147–1163 (2015)

    CrossRef  Google Scholar 

  7. Liu, M., Qiu, K., Che, F., Li, S., Hussain, B., Wu, L., Yue, C.P.: Towards indoor localization using visible light communication for consumer electronic devices. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 143–148. IEEE (2014)

    Google Scholar 

  8. Zhang, F., Qiu, K., Liu, M.: Asynchronous blind signal decomposition using tiny-length code for visible light communication-based indoor localization, pp. 2800–2805 (2015)

    Google Scholar 

  9. Qiu, K., Zhang, F., Liu, M.: Visible light communication-based indoor localization using gaussian process, pp. 3125–3130 (2015)

    Google Scholar 

  10. Qiu, K., Zhang, F., Liu, M.: Let the light guide us: VLC-based localization. IEEE Robot. Autom. Mag. 23(4), 174–183 (2016)

    CrossRef  Google Scholar 

  11. Vadeny, D., Chen, M., Huang, E., Elgala, H.: VSLAM and VLC based localization

    Google Scholar 

  12. Williams, B., Cummins, M., Neira, J., Newman, P., Reid, I., Tardós, J.: A comparison of loop closing techniques in monocular SLAM. Robot. Auton. Syst. 57(12), 1188–1197 (2009)

    CrossRef  Google Scholar 

  13. Cummins, M., Newman, P.: FAB-MAP: probabilistic localization and mapping in the space of appearance. Int. J. Robot. Res. 27(6), 647–665 (2008)

    CrossRef  Google Scholar 

  14. Mur-Artal, R., Tardós, J.D.: Fast relocalisation and loop closing in keyframe-based SLAM. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 846–853. IEEE (2014)

    Google Scholar 

  15. Liu, M., Siegwart, R.: Topological mapping and scene recognition with lightweight color descriptors for an omnidirectional camera. IEEE Trans. Rob. 30(2), 310–324 (2014)

    CrossRef  Google Scholar 

  16. Whelan, T., Kaess, M., Leonard, J.J., McDonald, J.: Deformation-based loop closure for large scale dense RGB-D SLAM. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 548–555. IEEE (2013)

    Google Scholar 

  17. Glover, A.J., Maddern, W.P., Milford, M.J., Wyeth, G.F.: FAB-MAP + RATSLAM: appearance-based SLAM for multiple times of day. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 3507–3512. IEEE (2010)

    Google Scholar 

  18. Quigley, M., Conley, K., Gerkey, B., 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, Kobe, vol. 3, p. 5 (2009)

    Google Scholar 

  19. Arumugam, R., Enti, V.R., Bingbing, L., Xiaojun, W., Baskaran, K., Kong, F.F., Senthil Kumar, A., Meng, K.D., Kit, G.W.: DAvinCi: a cloud computing framework for service robots. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 3084–3089. IEEE (2010)

    Google Scholar 

  20. Markus, W., Michael, B., Javier, C., d’Andrea, R., Elfring, J., Galvez-Lopez, D., Häussermann, K., Janssen, R., Montiel, J.M.M., Perzylo, A., et al.: RoboEarth. IEEE Robot. Autom. Mag. 18(2), 69–82 (2011)

    CrossRef  Google Scholar 

  21. Hunziker, D., Gajamohan, M., Waibel, M., D’Andrea, R.: Rapyuta: the RoboEarth cloud engine. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 438–444. IEEE (2013)

    Google Scholar 

  22. Mohanarajah, G., Usenko, V., Singh, M., D’Andrea, R., Waibel, M.: Cloud-based collaborative 3D mapping in real-time with low-cost robots. IEEE Trans. Autom. Sci. Eng. 12(2), 423–431 (2015)

    CrossRef  Google Scholar 

  23. Riazuelo, L., Civera, J., Montiel, J.M.M.: C2TAM: a first approach to a cloud framework for cooperative tracking and mapping

    Google Scholar 

  24. Labbe, M., Michaud, F.: Appearance-based loop closure detection for online large-scale and long-term operation. IEEE Trans. Robot. 29(3), 734–745 (2013)

    CrossRef  Google Scholar 

  25. Wang, L., Liu, M., Meng, M.Q.H.: A hierarchical auction-based mechanism for real-time resource allocation in cloud robotic systems. IEEE Trans. Syst. Man Cybern. 1–12 (2016)

    Google Scholar 

  26. Lubbers, P., Albers, B., Smith, R., Salim, F.: Pro HTML5 Programming: Powerful APIs for Richer Internet Application Development. Apress, Berkely (2010)

    CrossRef  Google Scholar 

  27. 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)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Jianhao Jiao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Jiao, J., Yun, P., Liu, M. (2017). A Cloud-Based Visual SLAM Framework for Low-Cost Agents. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68344-7

  • Online ISBN: 978-3-319-68345-4

  • eBook Packages: Computer ScienceComputer Science (R0)