Topological Mapping with Image Sequence Partitioning

  • Hemanth Korrapati
  • Jonathan Courbon
  • Youcef Mezouar
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 193)

Abstract

Topological maps are vital for fast and accurate localization in large environments. Sparse topological maps can be constructed by partitioning a sequence of images acquired by a robot, according to their appearance. All images in a partition have similar appearance and are represented by a node in a topological map. In this paper, we present a topological mapping framework which makes use of image sequence partitioning (ISP) to produce sparse maps. The framework facilitates coarse loop closure at node level and a finer loop closure at image level. Hierarchical inverted files (HIF) are proposed which are naturally adaptable to our sparse topological mapping framework and enable efficient loop closure. Computational gain attained in loop closure with HIF over sparse topological maps is demonstrated. Experiments are performed on outdoor environments using an omni-directional camera.

Keywords

Topological Mapping Omni-directional Vision Loop Closure 

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References

  1. 1.
    Kosecka, J., Murillo, A.C., Campos, P., Guerrero, J.J.: Gist vocabularies in omnidirectional images for appearance based mapping and localization. In: 10th IEEE Workshop on Omnidirectional Vision, Camera Networks and Non-Classical Cameras (OMNIVIS), Held with Robotics, Science and Systems (2010)Google Scholar
  2. 2.
    Angeli, A., Filliat, D., Doncieux, S., Meyer, J.-A.: A fast and incremental method for loop-closure detection using bags of visual words. IEEE Transactions on Robotics, Special Issue on Visual SLAM (2008)Google Scholar
  3. 3.
    Cummins, M., Newman, P.: FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance. The International Journal of Robotics Research 27(6), 647–665 (2008)CrossRefGoogle Scholar
  4. 4.
    Cummins, M., Newman, P.: Highly scalable appearance-only slam fab-map 2.0. In: Robotics Science and Systems (RSS), Seattle, USA (June 2009)Google Scholar
  5. 5.
    Koseck, J., Li, F., Yang, X.: Global localization and relative positioning based on scale-invariant keypoints. Robotics and Autonomous Systems 52(1), 27–38 (2005)CrossRefGoogle Scholar
  6. 6.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  7. 7.
    Nourani-Vatani, N., Pradalier, C.: Scene change detection for topological localization and mapping. In: IEEE/RSJ Intl. Conf. on Intelligent Robotics and Systems, IROS (2010)Google Scholar
  8. 8.
    Tapus, A., Siegwart, R.: Incremental robot mapping with fingerprints of places. In: IEEE/RSJ Intl. Conf. on Intelligent Robotics and Systems (IROS), pp. 2429–2434 (2005)Google Scholar
  9. 9.
    Valgren, C., Duckett, T., Lilienthal, A.: Incremental spectral clustering and its application to topological mapping. In: Proc. IEEE Int. Conf. on Robotics and Automation, pp. 4283–4288 (2007)Google Scholar
  10. 10.
    Zivkovic, Z., Booij, O., Kröse, B.: From images to rooms. Robot. Auton. Syst. 55, 411–418 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hemanth Korrapati
    • 1
  • Jonathan Courbon
    • 1
  • Youcef Mezouar
    • 1
    • 2
  1. 1.Institut PascalClermont Université, Université Blaise PascalClermont-FerrandFrance
  2. 2.CNRS, UMR 6602, IPAubiéreFrance

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