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Mozos, Oscar Martinez; Mizutani, Hitoshi; Kurazume, Ryo; Hasegawa, Tsutomu (2012)
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Sensors (Basel, Switzerland)
Languages: English
Types: Article
Subjects: Kinect sensor, service robots, TP1-1185, Chemical technology, H671 Robotics, Article, place categorization, G760 Machine Learning
The categorization of places in indoor environments is an important capability for service robots working and interacting with humans. In this paper we present a method to categorize different areas in indoor environments using a mobile robot equipped with a Kinect camera. Our approach transforms depth and grey scale images taken at each place into histograms of local binary patterns (LBPs) whose dimensionality is further reduced following a uniform criterion. The histograms are then combined into a single feature vector which is categorized using a supervised method. In this work we compare the performance of support vector machines and random forests as supervised classifiers. Finally, we apply our technique to distinguish five different place categories: corridors, laboratories, offices, kitchens, and study rooms. Experimental results show that we can categorize these places with high accuracy using our approach.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Martinez Mozos, O. Semantic Place Labeling with Mobile Robots; Springer-Verlag: Berlin/Heidelberg, Germany, 2010.
    • Semantic Modelling of Space. In Cognitive Systems, 1st ed.; Christensen, H.I., Sloman, A., Kruijff, G.-J.M., Wyatt, J., Eds.; Springer: Berlin/Heidelberg, Germany, 2010, pp. 165-221.
    • Zender, H.; Mozos, O.M.; Jensfelt, P.; Kruijff, G.-J.M.; Burgard, W. Conceptual spatial representations for indoor mobile robots. Robot. Auton. Syst. 2008, 56, 493-502.
    • Wolf, D.F.; Sukhatme, G.S. Semantic mapping using mobile robots. IEEE Trans. Robot. 2008, 24, 245-258.
    • Nüchter, A.; Hertzberg, J. Towards semantic maps for mobile robots. Robot. Auton. Syst. 2008, 56, 915-926.
    • Multi-Hierarchical Semantic Maps for Mobile Robotics. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Alberta, Canada, 2005; pp. 2278-2283.
    • 7. Torralba, A.; Murphy, K.P.; Freeman, W.T.; Rubin, M.A. Context-Based Vision System for Place and Object Recognition. In Proceedings of the International Conference on Computer Vision, Nice, France, 2003; pp. 273-280.
    • 8. Kollar, T.; Roy, N. Utilizing Object-Object and Object-Scene Context when Planning to Find Things. In Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, Japan, 2009; pp. 2168-2173.
    • 9. Stachniss, C.; Mozos, O.M.; Burgard, W. Speeding-Up Multi-Robot Exploration by Considering Semantic Place Information. In Proceedings of the IEEE International Conference on Robotics and Automation, Orlando, FL, USA, 2006; pp. 1692-1697.
    • 10. Zender, H.; Jensfelt, P.; Kruijff, G.-J. Human- and Situation-Aware People Following. In Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication, Jeju, Korea, 2007; pp. 1131-1136.
    • 11. Galindo, C.; Fernández-Madrigal, J.A.; González, J.; Saffiotti, A. Robot task planning using semantic maps. Robot. Auton. Syst. 2008, 56, 955-966.
    • 12. Kruijff, G.-J.M.; Zender, H.; Jensfelt, P.; Christensen, H.I. Situated dialogue and spatial organization: What, where…and why? Int. J. Adv. Robot. Syst. 2007, 4, 125-138.
    • 13. Topp, E.A.; Hüttenrauch, H.; Christensen, H.I.; Severinson Eklundh, K. Acquiring a Shared Environment Representation. In Proceedings of the 1st ACM Conference on Human-Robot Interaction, Salt Lake City, UT, USA, 2006; pp. 361-362.
    • 14. Microsoft Kinect. Available online: http://www.xbox.com/en-us/kinect/ (access on 11 April 2012).
    • 15. Ojala, T.; Pietikäinen, M.; Mäenpää, T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24, 971-987.
    • 16. Wu, J.; Rehg, J.M. CENTRIST: A visual descriptor for scene categorization. IEEE T. Pattern Anal. 2011, 33, 1489-1501.
    • 17. Ranganathan, A. PLISS: Detecting and Labeling Places Using Online Change-Point Detection. In Proceedings of the Robotics: Science and Systems, Zaragoza, Spain, 2010.
    • 18. Vapnik, V.N. An overview of statistical learning theory. IEEE Trans. Neural Netw. 1999, 10, 988-999.
    • 19. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32.
    • 20. Mozos, O.M.; Stachniss, C.; Burgard, W. Supervised Learning of Places from Range Data Using Adaboost. In Proceedings of the IEEE International Conference on Robotics and Automation, Barcelona, Spain, 2005; pp. 1742-1747.
    • 21. Friedman, S.; Pasula, H.; Fox, D. Voronoi Random Fields: Extracting the Topological Structure of Indoor Environments via Place Labeling. In Proceedings of the International Joint Conference on Artificial Intelligence, Hyderabad, India, 2007.
    • 22. Brunskill, E.; Kollar, T.; Roy, N. Topological Mapping Using Spectral Clustering and Classification. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA, 2007; pp. 3491-3496.
    • 23. Shi, L.; Kodagoda, S.; Dissanayake, G. Laser Range Data Based Semantic Labeling of Places. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, 2010; pp. 5941-5946.
    • 24. Rottmann, A.; Martinez-Mozos, O.; Stachniss, C.; Burgard, W. Semantic Place Classification of Indoor Environments with Mobile Robots Using Boosting. In Proceedings of the National Conference on Artificial Intelligence, Pittsburgh, PA, USA, 2005; pp. 1306-1311.
    • 25. Pronobis, A.; Mozos, O.M.; Caputo, B.; Jensfelt, P. Multi-modal semantic place classification. Int. J. Robot. Res. 2010, 29, 298-320.
    • 26. Ojala, T.; Pietikainen, M.; Harwood, D. A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 1996, 29, 51-59.
    • 27. Zabih, R.; Woodfill, J. Non-Pparametric Local Transforms for Computing Visual Correspondence. In Proceedings of the European Conference of Computer Vision, Stockholm, Sweden, 1994; pp. 151-158.
    • 28. Lazebnik, S.; Schmid, C.; Ponce, J. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New York, NY, USA, 2006; pp. 2169-2178.
    • 29. Cortes, C.; Vapnik, V. Support-vector network. Mach. Learn. 1995, 20, 273-297.
    • 30. Bishop, C.M. Pattern Recognition and Machine Learning; Springer: New York, NY, USA, 2006.
    • 31. Knerr, S.; Personnaz, L.; Dreyfus, G. Single-layer Learning Revisited: A Stepwise Procedure for Building and Training a Neural Network. In Neurocomputing: Algorithms, Architectures and Applications; Fogelman, F., Hérault, J., Eds.; Springer-Verlag: Berlin, Germany, 1990; Volume F68 of NATO ASI Series, pp. 41-50.
    • 32. Chang, C.-C.; Lin, C.-J. LIBSVM: A library for support vector machines, ACM Trans. Intell. Syst. Technol. 2011, 2, 27:1-27:27.
    • 33. Hsu, C.-W.; Chang, C.-C.; Lin, C.-J. A Practical Guide to Support Vector Classification. Available online: http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf (access on 1 October 2011).
    • 34. Hall, M.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P.; Witten, I.H. The WEKA data mining software: An update. ACM SIGKDD Explor. Newsl. 2009, 11, 10-18.
    • 35. RGB-D Place Dataset, http://robotics.ait.kyushu-u.ac.jp/~kurazume/r-cv-e.html#c10 (accessed on 21 May 2012)
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.