Skip to main content
SpringerLink
Log in

GLANS: GIS Based Large-Scale Autonomous Navigation System

  • Conference paper
  • First Online:
Advances in Swarm Intelligence (ICSI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10942))

Included in the following conference series:

Abstract

The simultaneous localization and mapping (SLAM) systems are widely used for self-localization of a robot, which is the basis of autonomous navigation. However, the state-of-art SLAM systems cannot suffice when navigating in large-scale environments due to memory limit and localization errors. In this paper, we propose a Geographic Information System (GIS) based autonomous navigation system (GLANS). In GLANS, a topological path is suggested by GIS database and a robot can move accordingly while being able to detect the obstacles and adjust the path. Moreover, the mapping results can be shared among multi-robots to re-localize a robot in the same area without GPS assistance. It has been proved functioning well in the simulation environment of a campus scenario.

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

Access this chapter

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Institutional subscriptions

References

  1. Ip, Y.L., et al.: Segment-based map building using enhanced adaptive fuzzy clustering algorithm for mobile robot applications. J. Intell. Robot. Syst. 35(3), 221–245 (2002)

    Article  Google Scholar 

  2. Dissanayake, M.W.M.G., et al.: A solution to the simultaneous localization and map building (SLAM) problem. IEEE Trans. Robot. Autom. 17(3), 229–241 (2001)

    Article  Google Scholar 

  3. Castellanos, J.A., et al.: The SPmap: a probabilistic framework for simultaneous localization and map building. IEEE Trans. Robot. Autom. 15(5), 948–952 (1999)

    Article  Google Scholar 

  4. Shi, C.X., et al.: Topological map building and navigation in large-scale environments. Robot 29(5), 433–438 (2007)

    Google Scholar 

  5. Gutiérrez, P., et al.: Mission planning, simulation and supervision of unmanned aerial vehicle with a GIS-based framework. In: Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics, ICINCO 2006, Setúbal, Portugal, pp. 310–317. DBLP, August 2006

    Google Scholar 

  6. He, X.: Vision/odometer autonomous navigation based on rat SLAM for land vehicles. In: Proceedings of 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics (2015)

    Google Scholar 

  7. Liu, D.X.: A research on LADAR-vision fusion and its application in cross country autonomous navigation vehicle. National University of Defense Technology (2009)

    Google Scholar 

  8. Lan, Y., Liu, W.W., Dong, W.: Research on rule editing and code generation for the high-level decision system of unmanned vehicles. Comput. Sci. Eng. 37(8), 1510–1516 (2015)

    Google Scholar 

  9. Quigley, M., Conley, K., Gerkey, B., et al.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)

    Google Scholar 

  10. Konolige, K., Marder-Eppstein, E., Marthi, B.: Navigation in hybrid metric-topological maps. In: IEEE International Conference on Robotics and Automation, pp. 3041–3047. IEEE (2011)

    Google Scholar 

  11. Lekkerkerker, C.J.: Gaining by forgetting: towards long-term mobile robot autonomy in large scale environments using a novel hybrid metric-topological mapping system (2014)

    Google Scholar 

  12. Zheng, J., et al.: A PostGIS-based pedestrian way finding module using OpenStreetMap data 12, 1–5 (2013)

    Google Scholar 

  13. Zhang, L., He, X.: Route Search Base on pgRouting. In: Wu, Y. (ed.) ECCV 2016. AISC, vol. 115, pp. 1003–1007. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-25349-2_133

    Chapter  Google Scholar 

  14. Krzyżek, R., Skorupa, B.: The influence of application a simplified transformation model between reference frames ECEF and ECI onto prediction accuracy of position and velocity of GLONASS satellites. Rep. Geodesy & Geoinformatics 99(1), 19–27 (2015)

    Article  Google Scholar 

  15. Huang, L.: ON NEU (ENU) coordinate system. J. Geodesy Geodyn. (2006). Tianjin

    Google Scholar 

  16. Grisetti, G., Stachniss, C., Burgard, W.: Improving grid-based SLAM with Rao-Blackwellized particle filters by adaptive proposals and selective resampling. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2005)

    Google Scholar 

  17. Macleod, C.D.: An Introduction to Using GIS in Marine Biology: Supplementary Workbook Seven An Introduction to Using QGIS (Quantum GIS). Pictish Beast Publications (2015)

    Google Scholar 

  18. Song, X.: Reading of GIS spatial data format. J. Cap. Normal Univ. (2006)

    Google Scholar 

  19. Luo, R., Hong, B.: Coevolution based adaptive Monte Carlo localization (CEAMCL). Int. J. Adv. Robot. Syst. 1(1), 183–190 (2004)

    Google Scholar 

  20. https://github.com/xxx. (for anonymous demand)

  21. Yuan, D., et al.: The coordinate transformation method and accuracy analysis in GPS measurement. Procedia Environ. Sci. Part A 12, 232–237 (2012)

    Article  Google Scholar 

  22. Tang, M., Mao, X., Guessoum, Z.: Research on an infectious disease transmission by flocking birds. Sci. World J. 2013(12), 196823 (2013)

    Google Scholar 

  23. Tang, M., Zhu, H., Mao, X.: A lightweight social computing. Approach to emergency management policy selection. IEEE Trans. Syst. Man Cybern. Syst. 1(1–2), 1–13 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of High-Performance Computing, College of Computer, National University of Defensive Technology, Deya Street No. 109, Changsha, 410001, China

    Manhui Sun, Shaowu Yang & Henzhu Liu

Authors
  1. Manhui Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shaowu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Henzhu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manhui Sun .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Tongji University, Shanghai, China

    Qirong Tang

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sun, M., Yang, S., Liu, H. (2018). GLANS: GIS Based Large-Scale Autonomous Navigation System. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93818-9_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93817-2

  • Online ISBN: 978-3-319-93818-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation