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How Do You Help a Robot to Find a Place? A Supervised Learning Paradigm to Semantically Infer about Places

  • Ioannis Kostavelis
  • Angelos Amanatiadis
  • Antonios Gasteratos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8073)

Abstract

In this paper a visual place recognition algorithm suitable for semantic inference is presented. It combines place and object classification attributes suitable for the recognition of congested and cluttered scenes. The place learning task is undertaken by a method capable of abstracting appearance information from the places to be memorized. The detected visual features are treated as a bag of words and quantized by a clustering algorithm to form a visual vocabulary of the explored places. Each query image is represented by a consistency histogram spread over the memorized vocabulary. Simultaneously, an object recognition approach based on Hierarchical Temporal Memory network, updates the robot’s belief of its current position exploiting the features of scattered objects within the scene. The input images which are introduced to the network undergo a saliency computation step and are subsequently thresholded based on an entropy metric for detecting multiple objects. The place and object decisions are fused by voting to infer the semantic attributes of a particular place. The efficiency of the proposed framework has been experimentally evaluated on a real dataset and proved capable of accurately recognizing multiple dissimilar places.

Keywords

place recognition HTM saliency map semantics robot navigation 

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References

  1. 1.
    Pronobis, A., Martínez Mozos, O., Caputo, B., Jensfelt, P.: Multi-modal semantic place classification. The International Journal of Robotics Research, IJRR 29(2-3), 298–320 (2010)CrossRefGoogle Scholar
  2. 2.
    Torralba, A., Murphy, K., Freeman, W., Rubin, M.: Context-based vision system for place and object recognition. In: International Conference on Computer Vision, ICCV 2003, pp. 273–280. IEEE (2003)Google Scholar
  3. 3.
    Zhang, J., Marszałek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. International Journal of Computer Vision 73(2), 213–238 (2007)CrossRefGoogle Scholar
  4. 4.
    Filliat, D.: A visual bag of words method for interactive qualitative localization and mapping. In: International Conference on Robotics and Automation, ICRA 2007, pp. 3921–3926. IEEE (2007)Google Scholar
  5. 5.
    Fraundorfer, F., Engels, C., Nistér, D.: Topological mapping, localization and navigation using image collections. In: International Conference on Intelligent Robots and Systems, IROS 2007, pp. 3872–3877. IEEE (2007)Google Scholar
  6. 6.
    Fazl-Ersi, E., Tsotsos, J.: Histogram of oriented uniform patterns for robust place recognition and categorization. The International Journal of Robotics Research 31(4), 468–483 (2012)CrossRefGoogle Scholar
  7. 7.
    Vasudevan, S., Siegwart, R.: Bayesian space conceptualization and place classification for semantic maps in mobile robotics. Robotics and Autonomous Systems 56(6), 522–537 (2008)CrossRefGoogle Scholar
  8. 8.
    Hawkins, J., Blakeslee, S.: On intelligence: How a new understanding of the brain will lead to the creation of truly intelligent machines. Henry Holt & Company, New York (2004)Google Scholar
  9. 9.
    Kostavelis, I., Gasteratos, A.: On the optimization of hierarchical temporal memory. Pattern Recognition Letters 33(5), 670–676 (2012)CrossRefGoogle Scholar
  10. 10.
    Charalampous, K., Kostavelis, I., Amanatiadis, A., Gasteratos, A.: Sparse deep-learning algorithm for recognition and categorisation. Electronics Letters 48(20), 1265–1266 (2012)CrossRefGoogle Scholar
  11. 11.
    Kostavelis, I., Nalpantidis, L., Gasteratos, A.: Object recognition using saliency maps and htm learning. In: IEEE International Conference on Imaging Systems and Techniques, IST 2012, pp. 528–532. IEEE (2012)Google Scholar
  12. 12.
    Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV 2004, vol. 1, p. 22 (2004)Google Scholar
  13. 13.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
  14. 14.
    Pronobis, A., Caputo, B., Jensfelt, P., Christensen, H.I.: A realistic benchmark for visual indoor place recognition. Robotics and Autonomous Systems 58(1), 81–96 (2010)CrossRefGoogle Scholar
  15. 15.
    Hou, X., Harel, J., Koch, C.: Image signature: Highlighting sparse salient regions. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(1), 194 (2012)CrossRefGoogle Scholar
  16. 16.
    Kostavelis, I., Gasteratos, A.: Cognitive Navigation Dataset, Group of Robotics and Cgnitive Systems (2012), http://robotics.pme.duth.gr/kostavelis/Dataset.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ioannis Kostavelis
    • 1
  • Angelos Amanatiadis
    • 1
  • Antonios Gasteratos
    • 1
  1. 1.Robotics and Automation Lab., Production and Management Engineering Dept.Democritus University of ThraceXanthiGreece

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