A Distributed and Multithreaded SLAM Architecture for Robotic Clusters and Wireless Sensor Networks

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 604)

Abstract

In this work, we propose an extremely efficient architecture for the Simultaneous Localization and Mapping (SLAM) problem. The architecture makes use of multithreading and workload distribution over a robotic cluster or a wireless sensor network (WSN) in order to parallelize the most widely used Rao-Blackwellized Particle Filter (RBPF) SLAM approach. We apply the method in common computers found in robots and sensor networks, and evaluate the tradeoffs in terms of efficiency, complexity, load balancing and SLAM performance. It is shown that a significant gain in efficiency can be obtained. Furthermore, the method enables us to raise the workload up to values that would not be possible in a single robot solution, thus gaining in localization precision and map accuracy. All the results are extracted from frequently used SLAM datasets available in the Robotics community and a real world testbed is described to show the potential of using the proposed philosophy.

Keywords

Computation sharing Simultaneous localization and mapping  Cooperative robotics Distributed computing Wireless sensor networks 

References

  1. 1.
    Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: a factored solution to the simultaneous localization and mapping problem. In: AAAI National Conference on Artificial Intelligence (2002)Google Scholar
  2. 2.
    Agarwal, P., Tipaldi, G.D., Spinello, L., Stachniss, C., Burgard, W.: Robust map optimization using dynamic covariance scaling. In: Proceedings of the International Conference on Robotics and Automation (ICRA 2013), Karlsruhe, Germany, May 6–10 (2013)Google Scholar
  3. 3.
    Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with Rao-Blackwellized particle filters. IEEE Trans. Robot. 23(1), 34-46 (2007)Google Scholar
  4. 4.
    Marjovi, A., Choobdar, S., Marques, L.: Robotic clusters: multirobot systems as computer clusters: a topological map merging demonstration. Robot. Auton. Syst. 60(9), 1191–1204 (2012)CrossRefGoogle Scholar
  5. 5.
    Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part I. IEEE Robot. Autom. Mag. 13(2), 99–110 (2006)CrossRefGoogle Scholar
  6. 6.
    Dissanayake, D., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A solution to the simultaneous localization and map building (SLAM) problem. IEEE Trans. Robot. Autom. 17(3), 229-241 (2001)Google Scholar
  7. 7.
    Thrun, S., Montemerlo, M.: The graphSLAM algorithm with applications to large-scale mapping of urban structures. Int. J. Robot. Res. 25, 403–429 (2006)Google Scholar
  8. 8.
    Kohlbrecher, S., Meyer, J., Von Stryk, O., Klingauf, U.: A flexible and scalable SLAM system with full 3D motion estimation. In: Proceedings of the International Symposium on Safety, Security and Rescue Robotics (2011)Google Scholar
  9. 9.
    Pedrosa, E., Lau, N., Pereira, A.: Online SLAM based on a fast scan-matching algorithm. In: Correia, L., Reis, L.P., Cascalho, J. (eds.) Progress in Artificial Intelligence, Lecture Notes in Computer Science, vol. 8154, pp. 295–306, Springer, Berlin (2013)Google Scholar
  10. 10.
    Machado Santos, J., Couceiro, M.S., Portugal, D., Rocha, R.P.: A sensor fusion layer to cope with reduced visibility in SLAM. In: Journal of Intelligent and Robotic Systems (JINT), Special Issue on Autonomous Robot Systems, Springer, London (2015)Google Scholar
  11. 11.
    Chandrakasan, A.P., Potkonjak, M., Mehra, R., Rabaey, J., Brodersen, R.W.: Optimizing power using transformations. IEEE Trans. Comput. Aided Des. Integr. Circ. Syst. 14(1), 12–31 (1995)CrossRefGoogle Scholar
  12. 12.
    El Hamzaoui, O., Steux, B.: A fast scan matching for grid-based laser SLAM using streaming SIMD extensions. In: Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010), Singapore, 7–10 Dec 2010Google Scholar
  13. 13.
    Clipp, B., Lim, J., Frahm, J., Pollefeys, M.: Parallel, real-time visual SLAM. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3961–3968, Taipei, Taiwan, Oct 2010Google Scholar
  14. 14.
    Par, K., Tosun, O.: Parallelization of particle filter based localization and map matching algorithms on multicore/manycore architectures. In: Proceedings of the IEEE Intelligent Vehicles Symposium. Baden, Germany, June 2011Google Scholar
  15. 15.
    Miah, M.S., Bolic, M.: Parallel implementation of modified rao-blackwellised particle filter. University of Ottawa, Technical Report (2009)Google Scholar
  16. 16.
    Riazuelo, L., Civera, J., Montiel, J.M.M.: C\(^2\)TAM: a cloud framework for cooperative tracking and mapping. Robot. Auton. Syst 62(4), 401–413 (2014)Google Scholar
  17. 17.
    Arumugam, R., Enti, V.R., Bingbing, L., Xiaojun, W., Baskaran, K., Kong, F.F., Kumar, A.S., 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, Anchorage, Alaska, USA (2010)Google Scholar
  18. 18.
    Siegwart, R., Balmer, P., Portal, C., Wannaz, C., Blank, R., Caprari, G.: RobOnWeb: a setup with mobile mini-robots on the web. In: An Introduction to Online Robots, MIT Press, In Beyond Webcams (2002)Google Scholar
  19. 19.
    Dorigo, M., Floreano, D., Gambardella, L.M., Mondada, F., Nolfi, S., Baaboura, T., Birattari, M., et al.: Swarmanoid: a novel concept for the study of heterogenous robotic swarms. IEEE Robot. Autom. Mag. 20(4), 60–71 (2013)Google Scholar
  20. 20.
    Gouveia, B.D., Portugal, D., Marques, L.: Speeding up rao-blackwellized particle filter SLAM with a multithreaded architecture. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, USA, 14–18 Sep 2014Google Scholar
  21. 21.
    Gouveia, B.D., Portugal, D., Marques, L.: Computation sharing in distributed robotic systems:a case study on SLAM. Proc. IEEE Trans. Autom. Sci. Eng., Spec. Issue Cloud Robot. Autom. 12(2), 398–409 (2015)Google Scholar
  22. 22.
    Lazaro, M., Paz, L., Piniés, P., Castellanos, J., Grisetti, G.: Multi-robot SLAM using condensed measurements. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1069–1076. Tokyo, Japan, 3–7 Nov 2013Google Scholar
  23. 23.
    Eich, M., Hartanto, R., Kasperski, S., Natarajan, S., Wollenberg, J.: Towards coordinated multirobot missions for lunar sample collection in an unknown environment. J. Field Robot. 31(1), 35–74 (2014)CrossRefGoogle Scholar
  24. 24.
    Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., Ng, R.: ROS: an open-source Robot Operating System. In: International Conference on Robotics and Automation, WS on Open Source Software, Kobe, Japan (2009)Google Scholar
  25. 25.
    Machado Santos, J., Portugal., D., Rocha, R.P.: An evaluation of 2D SLAM techniques availabile in robot operating system. In: Proceedings of the: International Symposium on Safety, Security and Rescue Robotics, Linköping, Sweden (2013)Google Scholar
  26. 26.
    Kümmerle, R., Steder, B., Dornhege, C., Ruhnke, M., Grisetti, G., Stachniss, C., Kleiner, A.: On measuring the accuracy of SLAM algorithms. Auton. Robots 27(4) (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • David Portugal
    • 1
  • Bruno D. Gouveia
    • 1
  • Lino Marques
    • 1
  1. 1.Institute of Systems and Robotics (ISR)University of Coimbra (UC)CoimbraPortugal

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