Skip to main content
SpringerLink
Log in

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

  • Conference paper
Advanced Concepts for Intelligent Vision Systems (ACIVS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6474))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. Mikolajczyk, K., Schmid, C.: Scale and Affine invariant interest point detectors. IJCV 60(1), 63–86 (2004)

    Article  Google Scholar 

  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. 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. 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. 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. Canny, J.F.: A computational approach to edge detection. IEEE Transaction on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  15. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  16. Bay, H., Tuytelaars, T., Gool, L.V.: Surf: Speeded up robust features. Computer Vision and Image Understanding (CVIU) 110(3), 346–359 (2008)

    Article  Google Scholar 

  17. Smith, R.C., Cheeseman, P.: On the representation and estimation of spatial uncertainty. Int. J. of Robotics Research 5(4), 56–68 (1986)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. 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. 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. 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. 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. 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. 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. 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)

    Article  Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

  1. Instituto Universitario Investigación en Informática, P.O. Box. 99, 03080, Alicante, Spain

    Anna Romero & Miguel Cazorla

Authors
  1. Anna Romero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miguel Cazorla
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. DGA/D4S/MRIS, CEP/GIP, 16 bis avenue Prieur de la côte d’or, 94114, Arcueil, France

    Jacques Blanc-Talon

  2. Canon Information Systems Research Australia, Sydney, Australia

    Don Bone

  3. Telecommunications and Information processing (TELIN), Ghent University, St.-Pietersnieuwstraat 41, B9000, Ghent, Belgium

    Wilfried Philips

  4. CSIRO ICT Centre, ICT Centre, Po Box 76, NSW 1710, Epping, Sydney, Australia

    Dan Popescu

  5. Physics, University of Antwerp, Universiteitsplein 1; Building N. 2610, Wilrijk, Belgium

    Paul Scheunders

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Romero, A., Cazorla, M. (2010). Topological SLAM Using Omnidirectional Images: Merging Feature Detectors and Graph-Matching. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2010. Lecture Notes in Computer Science, vol 6474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17688-3_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17688-3_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17687-6

  • Online ISBN: 978-3-642-17688-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation