Topological SLAM Using Omnidirectional Images: Merging Feature Detectors and Graph-Matching

  • Anna Romero
  • Miguel Cazorla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6474)


Image feature extraction and matching is useful in many areas of robotics such as object and scene recognition, autonomous navigation, SLAM and so on. This paper describes a new approach to the problem of matching features and its application to scene recognition and topological SLAM. For that purpose we propose a prior image segmentation into regions in order to group the extracted features in a graph so that each graph defines a single region of the image. This image segmentation considers that the left part of the image is the continuation of the right part. The matching process will take into account the features and the structure (graph) using the GTM algorithm. Then, using this method of comparing images, we propose an algorithm for constructing topological maps. During the experimentation phase we will test the robustness of the method and its ability constructing topological maps. We have also introduced a new hysteresis behavior in order to solve some problems found in construction of the graph.


Topological Mapping Graph matching Visual features 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Smith, M., Baldwin, I., Churchill, W., Paul, R., Newman, P.: The New College Vision and Laser Data Set. I. J. Robotic Res. 28(5), 595–599 (2009)CrossRefGoogle Scholar
  2. 2.
    Deng, Y., Manjunath, B.S.: Unsupervised Segmentation of Color-Texture Regions in Images and Video. IEEE Trans. Pattern Anal. Mach. Intell. 23(8), 800–810 (2001)CrossRefGoogle Scholar
  3. 3.
    Aguilar, W., Frauel, Y., Escolano, F., Elena Martinez-Perez, M., Espinosa-Romero, A., Lozano, M.A.: A robust Graph Transformation Matching for non-rigid registration. Image Vis. Comput. 27(7), 897–910 (2009)CrossRefGoogle Scholar
  4. 4.
    Joo, H., Jeong, Y., Duchenne, O., Ko, S.-Y., Kweon, I.-S.: Graph-based Robust Shape Matching for Robotic Application. In: IEEE Int. Conf. on Robotics and Automation, Kobe, Japan (May 2009)Google Scholar
  5. 5.
    Azad, P., Asfour, T., Dillmann, R.: Combining Harris Interest Points and the SIFT Descriptor for Fas Scale-Invariant Object Recognition. In: IEEE Int. Conf. on Intelligent Robots and Systems, St. Lois, USA (October 2009)Google Scholar
  6. 6.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. IJCV 65(1/2), 43–72 (2005)CrossRefGoogle Scholar
  7. 7.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: BMVC, pp. 384–393 (2002)Google Scholar
  8. 8.
    Mikolajczyk, K., Schmid, C.: Scale and Affine invariant interest point detectors. IJCV 60(1), 63–86 (2004)CrossRefGoogle Scholar
  9. 9.
    Chen, X., Huang, Q., Hu, P., Li, M., Tian, Y., Li, C.: Rapid and Precise Object Detection based on Color Histograms and Adaptive Bandwidth Mean Shift. In: IEEE Int. Conf. on Intelligent Robots and Systems, St. Lois, USA (October 2009)Google Scholar
  10. 10.
    Wu, J., Christensen, H.I., Rehg, J.M.: Visual Place Categorization: Problem, Dataset, and Algoritm. In: IEEE Int. Conf. on Intelligent Robots and Systems, St. Lois, USA (October 2009)Google Scholar
  11. 11.
    Liu, M., Scaramuzza, D., Pradalier, C., Siegwart, R., Chen, Q.: Scene recognition with Omnidirectional Vision for Topological Map using Lightweight Adaptive Descriptors. In: IEEE Int. Conf. on Intelligent Robots and Systems, St. Lois, USA (October 2009)Google Scholar
  12. 12.
    Vaquez-Martin, R., Marfil, R., Bandera, A.: Affine image region detection and description. Journal of Physical Agents 4(1), 45–54 (2010)Google Scholar
  13. 13.
    Canny, J.F.: A computational approach to edge detection. IEEE Transaction on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)CrossRefGoogle Scholar
  14. 14.
    Smith, S.M., Brady, J.M.: SUSAN - A New Approach to Low Level Image Processing. International Journal of Computer Vision 23, 45–78 (1995)CrossRefGoogle Scholar
  15. 15.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  16. 16.
    Bay, H., Tuytelaars, T., Gool, L.V.: Surf: Speeded up robust features. Computer Vision and Image Understanding (CVIU) 110(3), 346–359 (2008)CrossRefGoogle Scholar
  17. 17.
    Smith, R.C., Cheeseman, P.: On the representation and estimation of spatial uncertainty. Int. J. of Robotics Research 5(4), 56–68 (1986)CrossRefGoogle Scholar
  18. 18.
    Smith, R., Self, M., Cheeseman, P.: Estimating uncertain spatial relationships in robotics. In: Cox, I.J., Wilfong, G.T. (eds.) Autonomous Robot Vehicles, pp. 167–193. Springer, Heidelberg (1990)CrossRefGoogle Scholar
  19. 19.
    Julier, S., Uhlmann, J.K.: A counter example to the theory of simulataneous localization and map buildin. In: ICRA, pp. 4238–4243. IEEE, Los Alamitos (2001)Google Scholar
  20. 20.
    Montemerlo, M., Thrun, S.: Simultaneous localization and mapping with unknown data association using FastSLAM. In: Proc. of Intl. Conf. on Robotics and Automation, Taiwan, vol. 2, pp. 1985–1991 (2003)Google Scholar
  21. 21.
    Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: A factored solution to the simultaneous localization and mapping problem. In: AAAI/IAAI, pp. 593–598 (2002)Google Scholar
  22. 22.
    Diosi, A., Kleeman, L.: Advanced Sonar and Laser Range Finder Fusion for Simultaneous Localization and Mapping. In: Proc. of Intl. Conf. on Intelligent Robots and Systems, Japan, vol. 2, pp. 1854–1859 (2004)Google Scholar
  23. 23.
    Valgren, C., Lilienthal, A.J., Duckett, T.: Incremental Topological Mapping Using Omnidirectional Vision. In: IROS, pp. 3441–3447. IEEE, Los Alamitos (2006)Google Scholar
  24. 24.
    Romero, A.M., Cazorla, M., Suau, P., Escolano, F.: Graph-Matching Based Method for scene recognition on Omnidirectional Images. In: IROS (2010) (in revision)Google Scholar
  25. 25.
    Romero, A.M., Cazorla, M.: Topological SLAM Using a Graph-Matching Based Method on Omnidirectional Images. In: X Workshop de Agentes Físicos, Valencia (2010)Google Scholar
  26. 26.
    Motard, E., Raducanu, B., Cadenat, V., VitriÃă, J.: Incremental On-Line Topological Map Learning for A Visual Homing Application. In: ICRA, pp. 2049–2054. IEEE, Los Alamitos (2007)Google Scholar
  27. 27.
    Goedeme, T., Nuttin, M., Tuytelaars, T., Van Gool, L.J.: Omnidirectional Vision Based Topological Navigation. International Journal of Computer Vision 74(3), 219–236 (2007)CrossRefGoogle Scholar
  28. 28.
    Valgren, C., Duckett, T., Lilienthal, A.J.: Incremental Spectral Clustering and Its Application To Topological Mapping. In: ICRA, pp. 4283–4288. IEEE, Los Alamitos (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Anna Romero
    • 1
  • Miguel Cazorla
    • 1
  1. 1.Instituto Universitario Investigación en InformáticaAlicanteSpain

Personalised recommendations