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Automatic Extraction of Structural Representations of Environments

  • Roberto Capobianco
  • Guglielmo Gemignani
  • Domenico Daniele Bloisi
  • Daniele Nardi
  • Luca Iocchi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)

Abstract

Robots need a suitable representation of the surrounding world to operate in a structured but dynamic environment. State-of-the-art approaches usually rely on a combination of metric and topological maps and require an expert to provide the knowledge to the robot in a suitable format. Therefore, additional symbolic knowledge cannot be easily added to the representation in an incremental manner. This work deals with the problem of effectively binding together the high-level semantic information with the low-level knowledge represented in the metric map by introducing an intermediate grid-based representation. In order to demonstrate its effectiveness, the proposed approach has been experimentally validated on different kinds of environments.

References

  1. 1.
    Fabrizi, E., Saffiotti, A.: Augmenting topology-based maps with geometric information. Robotics and Autonomous Systems 40(2) (2002) 91–97CrossRefGoogle Scholar
  2. 2.
    Buschka, P., Saffiotti, A.: A virtual sensor for room detection. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (2002) 637–642Google Scholar
  3. 3.
    Thrun, S.: Learning metric-topological maps for indoor mobile robot navigation. Artificial Intelligence 99(1) (1998) 21–71CrossRefzbMATHGoogle Scholar
  4. 4.
    Anguelov, D., Koller, D., Parker, E., Thrun, S.: Detecting and modeling doors with mobile robots. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). (2004) 3777–3784Google Scholar
  5. 5.
    Galindo, C., Saffiotti, A., Coradeschi, S., Buschka, P., Fernández-Madrigal, J., González, J.: Multi-hierarchical semantic maps for mobile robotics. In: Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS). (2005) 3492–3497Google Scholar
  6. 6.
    Choi, J., Choi, M., Nam, S.Y., Chung, W.K.: Autonomous topological modeling of a home environment and topological localization using a sonar grid map. Autonomous Robots 30(4) (2011) 351–368CrossRefGoogle Scholar
  7. 7.
    Nüchter, A., Wulf, O., Lingemann, K., Hertzberg, J., Wagner, B., Surmann, H.: 3D Mapping with Semantic Knowledge. In: RoboCup 2005: Robot Soccer World Cup IX. (2005) 335–346Google Scholar
  8. 8.
    Mozos, O.M., Stachniss, C., Burgard, W.: Supervised learning of places from range data using adaboost. In: Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on, IEEE (2005) 1730–1735Google Scholar
  9. 9.
    Martinez Mozos, O., Triebel, R., Jensfelt, P., Rottmann, A., Burgard, W.: Supervised semantic labeling of places using information extracted from sensor data. Robotics and Autonomous Systems 55(5) (2007) 391–402CrossRefGoogle Scholar
  10. 10.
    Goerke, N., Braun, S.: Building semantic annotated maps by mobile robots. In: Proceedings of the Conference Towards Autonomous Robotic Systems. (2009) 149–156Google Scholar
  11. 11.
    Brunskill, E., Kollar, T., Roy, N.: Topological mapping using spectral clustering and classification. In: Proceedings of IEEE/RSJ Conference on Robots and Systems (IROS). (2007) 3491–3496Google Scholar
  12. 12.
    Friedman, S., Pasula, H., Fox, D.: Voronoi random fields: Extracting the topological structure of indoor environments via place labeling. In: Proceedings of 19th International Joint Conference on Artificial Intelligence (IJCAI). (2007) 2109–2114Google Scholar
  13. 13.
    Wu, J., Christenseny, H.I., Rehg, J.M.: Visual place categorization: Problem, dataset, and algorithm. In: Proceedings of IEEE/RSJ Conference on Robots and Systems (IROS). (2009) 4763–4770Google Scholar
  14. 14.
    Mozos, O.M., Mizutani, H., Kurazume, R., Hasegawa, T.: Categorization of indoor places using the kinect sensor. Sensors 12(5) (2012) 6695–6711CrossRefGoogle Scholar
  15. 15.
    Pangercic, D., Pitzer, B., Tenorth, M., Beetz, M.: Semantic object maps for robotic housework-representation, acquisition and use. In: Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, IEEE (2012) 4644–4651Google Scholar
  16. 16.
    Meyer, F.: Color image segmentation. In: Image Processing and its Applications, 1992., International Conference on, IET (1992) 303–306Google Scholar
  17. 17.
    Rosenthal, S., Biswas, J., Veloso, M.: An effective personal mobile robot agent through symbiotic human-robot interaction. In: Proc. of 9th International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS). (2010)Google Scholar
  18. 18.
    Bastianelli, E., Bloisi, D., Capobianco, R., Cossu, F., Gemignani, G., Iocchi, L., Nardi, D.: On-line semantic mapping. In: Proceedings of the 16th International Conference on Advanced Robotics (ICAR). (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Roberto Capobianco
    • 1
  • Guglielmo Gemignani
    • 1
  • Domenico Daniele Bloisi
    • 1
  • Daniele Nardi
    • 1
  • Luca Iocchi
    • 1
  1. 1.Sapienza University of RomeRomaItaly

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