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Journal of Intelligent & Robotic Systems

, Volume 83, Issue 3–4, pp 409–428 | Cite as

Multi-Robot Localization and Mapping Based on Signed Distance Functions

  • Philipp KochEmail author
  • Stefan May
  • Michael Schmidpeter
  • Markus Kühn
  • Christian Pfitzner
  • Christian Merkl
  • Rainer Koch
  • Martin Fees
  • Jon Martin
  • Daniel Ammon
  • Andreas Nüchter
Article

Abstract

This publication describes a 2D Simultaneous Localization and Mapping approach applicable to multiple mobile robots. The presented strategy uses data of 2D LIDAR sensors to build a dynamic representation based on Signed Distance Functions. Novelties of the approach are a joint map built in parallel instead of occasional merging of smaller maps and the limited drift localization which requires no loop closure detection. A multi-threaded software architecture performs registration and data integration in parallel allowing for drift-reduced pose estimation of multiple robots. Experiments are provided demonstrating the application with single and multiple robot mapping using simulated data, public accessible recorded data, two actual robots operating in a comparably large area as well as a deployment of these units at the Robocup rescue league.

Keywords

Mobile robotics Multi-robot Rescue robotics SLAM 

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References

  1. 1.
    Robocup rescue german open 2015. https://www.robocupgermanopen.de/en/major/rescue. Online; accessed 15-November-2015
  2. 2.
    slam benchmarking. http://kaspar.informatik.uni-freiburg.de/slamEvaluation. Online; accessed 14-January-2015
  3. 3.
    Burgard, W., Moors, M., Fox, D., Simmons, R., Thrun, S.: Collaborative multi-robot exploration. In: IEEE International Conference on Robotics and Automation, 2000. Proceedings. ICRA ’00, vol. 1, pp 476–481 (2000), doi: 10.1109/ROBOT.2000.844100
  4. 4.
    Burgard, W., Moors, M., Stachniss, C., Schneider, F.: Coordinated multi-robot exploration. IEEE Trans. Robot. 21(3), 376–386 (2005)CrossRefGoogle Scholar
  5. 5.
    Chen, Y., Medioni, G.: Object Modeling by Registration of Multiple Range Images. In: 1991 IEEE International Conference On Robotics and Automation, 1991. Proceedings., vol. 3, pp 2724–2729 (1991)Google Scholar
  6. 6.
    Fox, D., Ko, J., Konolige, K., Limketkai, B., Schulz, D., Stewart, B.: Distributed Multi-Robot Exploration and Mapping. In: Proceedings of the IEEE, p 2006 (2006)Google Scholar
  7. 7.
    Granstrom, K., Callmer, J., Ramos, F., Nieto, J.: Learning to Detect Loop Closure from Range Data. In: IEEE International Conference On Robotics and Automation, 2009. ICRA ’09, pp 15–22 (2009)Google Scholar
  8. 8.
    Howard, A.: Multi-Robot Simultaneous Localization and Mapping Using Particle Filters. In: Proceedings of Robotics: Science and Systems, Cambridge, USA (2005)Google Scholar
  9. 9.
    Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., Fitzgibbon, A.: Kinectfusion: Real-Time 3D Reconstruction and Interaction Using a Moving Depth Camera. In: Proceedings of the ACM Symposium on User Interface Software and Technology (2011)Google Scholar
  10. 10.
    Kim, B., Kaess, M., Fletcher, L., Leonard, J., Bachrach, A., Roy, N., Teller, S.: Multiple Relative Pose Graphs for Robust Cooperative Mapping. In: IEEE International Conference on Robotics and Automation, ICRA, pp 3185–3192 (2010)Google Scholar
  11. 11.
    Koch, P., May, S., Schmidpeter, M., Kuhn, M., Pfitzner, C., Merkl, C., Koch, R., Fees, M., Martin, J., Nuchter, A.: Multi-robot localization and mapping based on signed distance functions. In: 2015 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp 77–82 (2015), doi: 10.1109/ICARSC.2015.18
  12. 12.
    Kohlbrecher, S., Meyer, J., von Stryk, O., Klingauf, U.: A Flexible and Scalable Slam System with Full 3D Motion Estimation. In: Proceedings IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR), IEEE (2011)Google Scholar
  13. 13.
    Konolige, K., Fox, D., Ortiz, C., Agno, A., Eriksen, M., Limketkai, B., Ko, J., Morisset, B., Schulz, D., Stewart, B., Vincent, R.: Centibots: Very Large Scale Distributed Robotic Teams. In: Khatib, MHA Jr., O. (ed.) ISER, Springer Tracts in Advanced Robotics, vol. 21, pp 131–140. Springer (2004)Google Scholar
  14. 14.
    Kümmerle, R., Steder, B., Dornhege, C., Ruhnke, M., Grisetti, G., Stachniss, C., Kleiner, A.: On measuring the accuracy of slam algorithms. Auton. Robot. 27(4), 387–407 (2009)CrossRefGoogle Scholar
  15. 15.
    May, S., Koch, P., Koch, R., Merkl, C., Pfitzner, C., Nüchter, A.: A Generalized 2D and 3D Multi-Sensor Data Integration Approach Based on Signed Distance Functions for Multi-Modal Robotic Mapping. In: VMV 2014: Vision, Modeling & Visualization, Darmstadt, Germany, 2014. Proceedings, pp 95–102 (2014)Google Scholar
  16. 16.
    Osher, S., Fedkiw, R.: Level Set Methods and Dynamic Implicit Surfaces (Applied Mathematical Sciences). 2003rd edn. Springer (2002)Google Scholar
  17. 17.
    Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. Int. J. Comput. Vis. 13(2), 119–152 (1994)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Philipp Koch
    • 1
    Email author
  • Stefan May
    • 1
  • Michael Schmidpeter
    • 1
  • Markus Kühn
    • 1
  • Christian Pfitzner
    • 1
  • Christian Merkl
    • 1
  • Rainer Koch
    • 1
  • Martin Fees
    • 1
  • Jon Martin
    • 1
  • Daniel Ammon
    • 1
    • 2
  • Andreas Nüchter
    • 2
  1. 1.Faculty of Electrical Engineering, Precision Engineering, Information TechnologyNuremberg Institute of Technology Georg Simon OhmNurembergGermany
  2. 2.Informatics VII: Robotics and TelematicsUniversity WuerzburgWürzburgGermany

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