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Journal of Intelligent & Robotic Systems

, Volume 77, Issue 1, pp 5–16 | Cite as

Pose Uncertainty in Occupancy Grids through Monte Carlo Integration

  • Daniek Joubert
  • Willie Brink
  • Ben Herbst
Article

Abstract

We consider the dense mapping problem where a mobile robot must combine range measurements into a consistent world-centric map. If the range sensor remains fixed relative to the robot, as is usually the case, some form of simultaneous localization and mapping (SLAM) must be implemented in order to estimate the robot’s pose (position and orientation relative to the map) at every time step. Such estimates are typically characterized by uncertainty, and for safe navigation it can be important for the map to reflect the extent of those uncertainties. We present a simple and computationally tractable means of incorporating the pose distribution returned by SLAM directly into an occupancy grid map. We also indicate how our mechanism for handling pose uncertainty fits naturally into an existing adaptive grid mapping algorithm, which is more memory efficient, and offer some improvements to that algorithm. We demonstrate the effectiveness and benefits of our approach using simulated as well as real-world data.

Keywords

Occupancy grid mapping Pose uncertainty Monte Carlo integration Adaptive grid mapping 

Mathematics Subject Classification (2010)

68T40 

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References

  1. 1.
    Castellanos, J., Montiel, J., Neira, J., Tardós, J.: The SPmap: A probabilistic framework for simultaneous localization and map building. In: IEEE International Conference on Robotics and Automation, pp. 948–953 (1999)Google Scholar
  2. 2.
    Thrun, S., Burgard, W., Fox, D.: A probabilistic approach to concurrent mapping and localization for mobile robots. Artif. Intell. 5, 253–271 (1998)Google Scholar
  3. 3.
    Thrun, S.: Exploration and model building in mobile robot domains. In: IEEE International Conference on Neural Networks, pp. 175–180 (1993)Google Scholar
  4. 4.
    Chantila, R., Laumond, J.: Position referencing and consistent world modeling for mobile robots. In: IEEE International Conference on Robotics and Automation, pp. 138–145 (1985)Google Scholar
  5. 5.
    Moravec, H., Elfes, A.: High resolution maps from wide angle sonar. In: IEEE International Conference on Robotics and Automation, pp. 116–121 (1985)Google Scholar
  6. 6.
    Thrun, S.: Robotic mapping: A survey. In: Lakemeyer, G., Nebel, B. (eds.) Exploring Artificial Intelligence in the New Millennium, pp 1–36. Morgan Kaufmann (2002)Google Scholar
  7. 7.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press (2005)Google Scholar
  8. 8.
    Ivanjko, E., Petrović, I.: Extended Kalman filter based mobile robot pose tracking using occupancy grid maps. In: IEEE Mediterranean Electrotechnical Conference, pp. 311–314 (2004)Google Scholar
  9. 9.
    Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with Rao-Blackwellized particle filters. IEEE Trans. Robot. 23, 34–46 (2007)CrossRefGoogle Scholar
  10. 10.
    O’Callaghan, S., Ramos, F., Durrand-Whyte, H.: Contextual occupancy maps incorporating sensor and location uncertainty. In: IEEE International Conference on Robotics and Automation, pp. 478–3485 (2010)Google Scholar
  11. 11.
    O’Callaghan, S., Ramos, F.: Gaussian process occupancy maps. Int. J. Robot. Res. 31(1), 42–62 (2012)CrossRefGoogle Scholar
  12. 12.
    Merali, R., Barfoot, T.: Patch map: A benchmark for occupancy grid algorithm evaluation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3481–3488 (2012)Google Scholar
  13. 13.
    Einhorn, E., Schröter, C., Gross, H.: Finding the adequate resolution for grid mapping – cell size locally adapting on-the-fly. In: IEEE International Conference on Robotics and Automation, pp. 1843–1848 (2011)Google Scholar
  14. 14.
    Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping (SLAM): Part I. IEEE Robot. Autom. Mag. 13(2), 99–110 (2006)CrossRefGoogle Scholar
  15. 15.
    Bishop, C.: Pattern Recognition and Machine Learning. Springer, Berlin (2006)MATHGoogle Scholar
  16. 16.
    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, pp. 593–598 (2002)Google Scholar
  17. 17.
    Karris, S.: Signals and Systems with MATLAB Applications. Orchard Publications (2003)Google Scholar
  18. 18.
    Andert, F.: Drawing stereo disparity images into occupancy grids: measurement model and fast implementation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5191–5197 (2009)Google Scholar
  19. 19.
    Beraldin, J.: Integration of laser scanning and close-range photogrammetry – the last decade and beyond. In: International Society for Photogrammetry and Remote Sensing Congress, pp. 972–983 (2004)Google Scholar
  20. 20.
    Rubinstein, E., Kroese, D.: Simulation and the Monte Carlo Method. Wiley (2008)Google Scholar
  21. 21.
    Ugarte, M., Militino, A., Arnholt, A.: Probability and Statistics with R. CRC Press (2008)Google Scholar
  22. 22.
    Brink, W., Van Daalen, C., Brink, W.: FastSLAM with stereo vision. In: 23rd Annual Symposium of the Pattern Recognition Association of South Africa, pp. 24–30 (2012)Google Scholar
  23. 23.
    Bonarini, A., Burgard, W., Fontana, G., Matteucci, M., Sorrenti, D., Tardos, J.: RAWSEEDS: Robotics advancement through web-publishing of sensorial and elaborated extensive data sets. In: Proceedings of IROS’06 Workshop on Benchmarks in Robotics Research (2006)Google Scholar
  24. 24.
    Kohlbrecher, S., Meyer, J., Von Stryk, O., Klingauf, U.: A flexible and scalable SLAM system with full 3D motion estimation. In: IEEE International Symposium on Safety, Security and Rescue Robotics, pp. 155–160 (2011)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Electrical and Electronic EngineeringStellenbosch UniversityStellenboschSouth Africa
  2. 2.Department of Mathematical SciencesStellenbosch UniversityStellenboschSouth Africa

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