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Semantic Mapping with a Probabilistic Description Logic

  • Rodrigo Polastro
  • Fabiano Corrêa
  • Fabio Cozman
  • Jun OkamotoJr.
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6404)

Abstract

Semantic mapping employs explicit labels to deal with sensor data in robotic mapping processes. In this paper we present a method for boosting performance of spatial mapping, through the use of a probabilistic ontology, expressed with a probabilistic description logic. Reasoning with this ontology allows segmentation and tagging of sensor data acquired by a robot during navigation; hence a robot can construct metric maps topologically. We report experiments with a real robot to validate our approach, thus moving closer to the goal of integrating mapping and semantic labeling processes.

Keywords

Mobile Robot Sensor Data Description Logic Semantic Mapping Probabilistic Inclusion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Thrun, S.: Exploring Artificial Intelligence in the New Millennium. In: Robotic mapping: a survey, pp. 1–36. Morgan Kaufmann Publishers, San Francisco (2003)Google Scholar
  2. 2.
    Agarwal, S., Snavely, N., Simon, I., Seitz, S.M., Szeliski, R.: Building rome in a day. In: IEEE 12th International Conference on Computer Vision, pp. 72–79 (2009)Google Scholar
  3. 3.
    Anguelov, D., Taskar, B., Chatalbashev, V., Koller, D., Gupta, D., Heitz, G., Ng, A.: Discriminative learning of Markov random fields for segmentation of 3d scan data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 169–176 (2005)Google Scholar
  4. 4.
    Nuechter, A.: 3D Robotic Mapping. Springer Tracts in Advanced Robotics (2009)Google Scholar
  5. 5.
    Galindo, C., Fernndez-Madrigal, J.-A., Gonzlez, J., Saffiotti, A.: Robot task planning using semantic maps. Robotics and Autonomous Systems 11, 955–966 (2008)CrossRefGoogle Scholar
  6. 6.
    Hertzberg, J., Saffiotti, A.: Using semantic knowledge in robotics. Robotics and Autonomous Systems 56, 875–877 (2008)CrossRefGoogle Scholar
  7. 7.
    Coradeschi, S., Saffiotti, A.: An introduction to the anchoring problem. Robotics and Autonomous Systems 43, 85–96 (2003)CrossRefGoogle Scholar
  8. 8.
    Limketkai, B., Liao, L., Fox, D.: Relational object maps for mobile robots. In: Proceedings of the International Joint Conference on Artificial Intelligence, vol. 1, pp. 1471–1476 (2005)Google Scholar
  9. 9.
    Taskar, B., Abbeel, P., Wong, M.-F., Koller, D.: Relation Markov Networks. In: Introduction to Statistical Relational Learning, pp. 175–199. MIT Press, Cambridge (2007)Google Scholar
  10. 10.
    Wang, J., Domingos, P.: Hybrid Markov logic networks. In: Proceedings of the 23rd National Conference on Artificial Intelligence, vol. 2, pp. 1106–1111 (2008)Google Scholar
  11. 11.
    Posner, I., Schroeter, D., Newman, P.: Using scene similarity for place labeling. In: Proceedings of the 10th International Symposium on Experimental Robotics (2006)Google Scholar
  12. 12.
    Zivkovic, Z., Booij, O., Krse, B.: From images to rooms. Robotics and Autonomous Systems 55, 411–418 (2007)CrossRefGoogle Scholar
  13. 13.
    Vasudevan, S., Gchter, S., Nguyen, V., Siegwart, R.: Cognitive maps for mobile robots - an object based approach. Robotics and Autonomous Systems 55, 359–371 (2007)CrossRefGoogle Scholar
  14. 14.
    Lowe, D.G.: Distinctive image features from scale-invariant key-points. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F.: Description Logic Handbook. Cambridge University Press, Cambridge (2002)zbMATHGoogle Scholar
  16. 16.
    Cozman, F.G., Polastro, R.B.: Loopy propagation in a probabilistic description logic. In: Greco, S., Lukasiewicz, T. (eds.) SUM 2008. LNCS (LNAI), vol. 5291, pp. 120–133. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Polastro, R.B., Cozman, F.G.: Inference in probabilistic ontologies with attributive concept descriptions and nominals. In: 4th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW) at the 7th International Semantic Web Conference, ISWC (2008)Google Scholar
  18. 18.
    Ding, Z., Peng, Y., Pan, R.: BayesOWL: Uncertainty modeling in semantic web ontologies. In: Soft Computing in Ontologies and Semantic Web. Studies in Fuzziness and Soft Computing, vol. 204, pp. 3–29. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  19. 19.
    Heinsohn, J.: Probabilistic description logics. In: Conference on Uncertainty in Artificial Intelligence, pp. 311–318 (1994)Google Scholar
  20. 20.
    Jaeger, M.: Probabilistic reasoning in terminological logics. In: Principles of Knowledge Representation (KR), pp. 461–472 (1994)Google Scholar
  21. 21.
    Koller, D., Levy, A.Y., Pfeffer, A.: P-CLASSIC: A tractable probablistic description logic. In: AAAI, pp. 390–397 (1997)Google Scholar
  22. 22.
    Lukasiewicz, T.: Expressive probabilistic description logics. Artificial Intelligence 172, 852–883 (2008)CrossRefzbMATHGoogle Scholar
  23. 23.
    Costa, P.C.G., Laskey, K.B.: PR-OWL: A framework for probabilistic ontologies. In: Conference on Formal Ontology in Information Systems (2006)Google Scholar
  24. 24.
    Sebastiani, F.: A probabilistic terminological logic for modelling information retrieval. In: Croft, W., Rijsbergen, C.V. (eds.) 17th Annual International ACM Conference on Research and Development in Information Retrieval (SIGIR), pp. 122–130. Springer, Dublin (1994)Google Scholar
  25. 25.
    Schmidt-Schauss, M., Smolka, G.: Attributive concept descriptions with complements. Artificial Intelligence 48, 1–26 (1991)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Rodrigo Polastro
    • 1
  • Fabiano Corrêa
    • 1
  • Fabio Cozman
    • 1
  • Jun OkamotoJr.
    • 1
  1. 1.Escola Politécnica da Universidade de São PauloBrazil

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