Advertisement

GLANS: GIS Based Large-Scale Autonomous Navigation System

  • Manhui SunEmail author
  • Shaowu Yang
  • Henzhu Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)

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.

Keywords

SLAM GIS database Navigation at large-scale 

References

  1. 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)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 4.
    Shi, C.X., et al.: Topological map building and navigation in large-scale environments. Robot 29(5), 433–438 (2007)Google Scholar
  5. 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 2006Google Scholar
  6. 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. 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. 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. 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. 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. 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. 12.
    Zheng, J., et al.: A PostGIS-based pedestrian way finding module using OpenStreetMap data 12, 1–5 (2013)Google Scholar
  13. 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_133CrossRefGoogle Scholar
  14. 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)Google Scholar
  15. 15.
    Huang, L.: ON NEU (ENU) coordinate system. J. Geodesy Geodyn. (2006). TianjinGoogle Scholar
  16. 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. 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. 18.
    Song, X.: Reading of GIS spatial data format. J. Cap. Normal Univ. (2006)Google Scholar
  19. 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. 20.
    https://github.com/xxx. (for anonymous demand)
  21. 21.
    Yuan, D., et al.: The coordinate transformation method and accuracy analysis in GPS measurement. Procedia Environ. Sci. Part A 12, 232–237 (2012)CrossRefGoogle Scholar
  22. 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. 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

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of High-Performance Computing, College of ComputerNational University of Defensive TechnologyChangshaChina

Personalised recommendations