Advertisement

Towards Exploiting the Advantages of Colour in Scan Matching

  • Fernando MartínEmail author
  • Jaime Valls Miró
  • Luis Moreno
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 253)

Abstract

Colour plays an important role in the perception systems of the human beings. In robotics, the development of new sensors has made it possible to obtain colour information together with depth information about the environment. The exploitation of this type of information has become more and more important in numerous tasks. In our recent work, we have developed an evolutionary-based scan matching method. The aim of this work is to modify this method by the introduction of colour properties, taking the first steps in studying how to use colour to improve the scan matching. In particular, we have applied a colour transition detection method based on the delta E divergence between neighbours in a scan. Our algorithm has been tested in a real environment and significant conclusions have been reached.

Keywords

Differential Evolution Scan Matching RGB-D Colour Properties Delta E 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Leonard, J.J., Durrant-Whyte, H.: Mobile Robot Localization by Tracking Geometric Beacons. IEEE Transaction on Robotics and Automation 7, 376–382 (1991)CrossRefGoogle Scholar
  2. 2.
    Smith, R., Self, M., Cheeseman, P.: Estimating Uncertain Spatial Relationships in Robotics. In: Proceedings of the Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI 1986), New York, NY, pp. 267–288. Elsevier Science (1986)Google Scholar
  3. 3.
    Martín, F., Triebel, R., Moreno, L., Siegwart, R.: Two different tools for three-dimensional mapping: DE-based scan matching and feature-based loop detection. Robotica (2013)Google Scholar
  4. 4.
    Storn, R., Price, K.: Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization 11, 341–359 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments. In: Proceedings of the Intl. Symp. on Experimental Robotics, ISER (2010)Google Scholar
  6. 6.
    Nüchter, A., Lingemann, K., Hertzberg, J.: 6D SLAM-3D Mapping Outdoor Environments. Journal of Field Robotics 24, 699–722 (2007)CrossRefzbMATHGoogle Scholar
  7. 7.
    Besl, P.J., McKay, N.D.: A method for registration of 3d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)CrossRefGoogle Scholar
  8. 8.
    Hähnel, D., Burgard, W., Thrun, S.: Learning compact 3d models of indoor and outdoor environments with a mobile robot. Robotics and Autonomous Systems 44, 15–27 (2003)CrossRefGoogle Scholar
  9. 9.
    Triebel, R., Pfaff, P., Burgard, W.: Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006 (2006)Google Scholar
  10. 10.
    Cole, D.M., Newman, P.M.: Using Laser Range Data for 3D SLAM in Outdoor Environments. In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2006 (2006)Google Scholar
  11. 11.
    Magnusson, M., Ducket, T.: A Comparison of 3D Registration Algorithms for Autonomous Underground Mining Vehicles. In: Proceedings of the Second European Conference on Mobile Robotics, Ancona, Italy (2005)Google Scholar
  12. 12.
    Biber, P., Straßer, W.: The Normal Distributions Transform: A New Approach to Laser Scan Matching. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2003 (2003)Google Scholar
  13. 13.
    Lu, F., Milios, E.: Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans. Journal of Intelligent and Robotic Systems 20, 249–275 (1997)CrossRefGoogle Scholar
  14. 14.
    Tomono, M.: A scan matching method using euclidean invariant signature for global localization and map building. In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2004 (2004)Google Scholar
  15. 15.
    Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. International Journal of Computer Vision 13, 119–152 (1994)CrossRefGoogle Scholar
  16. 16.
    Rusinkiewicz, S., Levoy, M.: Efficient Variants of the ICP Algorithm. In: Proceedings of the Third International Conference on 3D Digital Imaging and Modeling (2001)Google Scholar
  17. 17.
    Bosse, M., Zlot, R.: Map Matching and Data Association for Large-Scale 2D Laser Scan-Based SLAM. The International Journal of Robotics Research 27, 667–691 (2008)CrossRefGoogle Scholar
  18. 18.
    Johnson, A., Kang, S.B.: Registration and integration of textured 3-d data. In: International Conference on Recent Advances in 3-D Digital Imaging and Modeling (3DIM 1997) (1997)Google Scholar
  19. 19.
    May, S., Droeschel, D., Holz, D., Fuchs, S., Malis, E., Nüchter, A., Hertzberg, J.: Three-dimensional mapping with time-of-flight camers. Journal of Field Robotics 26, 11–12 (2009)CrossRefGoogle Scholar
  20. 20.
    Diosi, A., Kleeman, L.: Laser Scan Matching in Polar Coordinates with Application to SLAM. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2005 (2005)Google Scholar
  21. 21.
    Thrun, S., Burgard, W., Fox, D.: A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping. In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2000 (2000)Google Scholar
  22. 22.
    Ramos, F., Fox, D., Durrant-Whyte, H.: CRF-matching: Conditional random fields for feature-based scan matching. In: Proceedings of Robotics: Science and Systems, RSS 2007 (2007)Google Scholar
  23. 23.
    Izadi, S., Kim, D., Molyneaux, O.H.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 24th Annual ACM Symposium on User Interface Software and Technology (UIST 2011), pp. 559–568 (2011)Google Scholar
  24. 24.
    Newcombe, R.A., Davison, A.J., Izadi, S., Kohli, P., Hilliges, O., Shotton, J., Hodges, D.M.S., Kim, D., Fitzgibbon, A.: KinectFusion: Real-Time Dense Surface Mapping and Tracking. In: Proceedings of the 10th IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2011 (2011)Google Scholar
  25. 25.
    Hunter, R.S.: Photoelectric color-difference meter. In: Proceedings of the Winter Meeting of the Optical Society of America (1948)Google Scholar
  26. 26.
    Hunter, R.S.: Accuracy, precision, and stability of new photo-electric color-difference meter. In: Proceedings of the Thirty-Third Annual Meeting of the Optical Society of America (1948)Google Scholar
  27. 27.
    Moreno, L., Garrido, S., Mun̈oz, M.L.: Evolutionary Filter for Robust Mobile Robot Localization. Robotics and Autonomous Systems 54(7), 590–600 (2006)CrossRefGoogle Scholar
  28. 28.
    Martín, F., Moreno, L., Garrido, S., Blanco, D.: High-Accuracy Global Localization Filter for three-dimensional Environments. Robotica 30, 363–378 (2011)CrossRefGoogle Scholar
  29. 29.
    Magnusson, M., Nüchter, A., Lörken, C., Lilienthal, A.J., Hertzberg, J.: Evaluation of 3D Registration Reliability and Speed – A Comparison of ICP and NDT. In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2009 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Fernando Martín
    • 1
    Email author
  • Jaime Valls Miró
    • 2
  • Luis Moreno
    • 1
  1. 1.Carlos III UniversityMadridSpain
  2. 2.University of TechnologySydneyAustralia

Personalised recommendations