Probabilistic Contextual Situation Analysis

  • Guy Ramel
  • Roland Siegwart
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 46)


Mobile robots are gradually appearing in our daily environment. To navigate autonomously in real-world environments and interact with objects and humans, robots face various major technological challenges. Among the required key competencies of such robots is their ability to perceive the environment and reason about it, to plan appropriate actions. However, sensory information perceived from real-world situations is error prone and incomplete and thus often results in ambiguous interpretations. We propose a new approach for object recognition that incorporates visual and range information with spatial arrangement between objects (context information). It is based on using Bayesian networks to fuse and infer information from different data.In the proposed framework, we first extract potential objects from the scene image using simple features- characteristics like colour or the relation between height and width. This basic information is easy to extract but often results in ambiguous situations between similar objects. To resolve ambiguities among the detected objects, the relative spatial arrangement (context information) of the objects is used in a second step. Consider, for example, a cola can and a red trash can that are both cylindrical, have similar ratios between width and height and have very similar colours. Depending on their distances from the robot, they may be hard to distinguish. However, if we further consider their spatial arrangement with other objects, e.g. a table, they might be clearly differentiable, the cola can typically standing on the table and the trash can on the floor. This contextual information is therefore a very efficient way to increase drastically the reliability of object recognition and scene interpretation. Moreover, range information from a laser scanner and speech recognition offer complementary information to improve reliability further. Thus, an approach using laser range data to recognize places (such as corridors, crossings, rooms and doors) using Bayesian programming is also developed for both topological navigation in a typical indoor environment and object recognition.


Mobile Robot Bayesian Network Context Information Gesture Recognition Reference Object 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Beal, R., Jackson, T.: Neural computing: an introduction, Adam Hilger, Bristol, Philadelphia and New York (1990)Google Scholar
  2. Becker, A., Naïm, P.: Les réseaux bayésiens, Eyrolles, Paris (1999)Google Scholar
  3. Caplat, G.: Modélisation cognitive et résolution de problèmes. Press Polytechniques Universitaires Romandes, CH-1015 Lausanne (2002)zbMATHGoogle Scholar
  4. Davalo, É., Naïm, P.: Des réseaux de neurones, Eyrolles, Paris (1990)Google Scholar
  5. Delessert, A.: Introduction à la logique. Press Polytechniques Universitaires Romandes, CH-1015 Lausanne (1988)zbMATHGoogle Scholar
  6. Freeman, J.A., Skapura, D.M.: Neural Networks: Algorithms, Applications and Programming Techniques. Addison-Wesley, Reading (1991)zbMATHGoogle Scholar
  7. Jensen, B., Ramel, G., Siegwart, R.: Detecting semi-static objects with a laser scanner. In: Autonome Mobile Systeme (AMS 2003) (2003)Google Scholar
  8. Jensen, F.V.B.: An introduction to Bayesian Networks. UCL Press (1996)Google Scholar
  9. Kortenkamp, D., Huber, E., Bonasso, R.P.: Recognizing and interpreting gestures on a mobile robot. In: AAAI/IAAI, vol. 2, pp. 915–921 (1996),
  10. Lauritzen, S.L.: Graphical Models. Clarendon press, Oxford (1996)Google Scholar
  11. Lebeltel, O.: Programmation Bayésienne des robots. PhD thesis, Institut National Polytechnique de Grenoble (1999)Google Scholar
  12. Lebeltel, O., Bessière, P., Diard, J., Mazer, E.: Bayesian robot programming. Advanced Robotics 16(1), 49–79 (2004)Google Scholar
  13. McGuire, P., Fritsch, J., Steil, J., Röthling, F., Fink, A., Wachsmuth, S., Sagerer, G., Ritter, H.: Multi-modal human machine communication for instructing robot grasping task. In: IEEE/RSJ (2002)Google Scholar
  14. Murphy, K., Torralba, A., Eaton, D., Freeman, W.: Object Detection and Localization Using Local and Global Features. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds.) Toward Category-Level Object Recognition. LNCS, vol. 4170, pp. 382–400. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. Pavlovic, V.I.: Dynamique bayesian networks for information fusion with applications to human-computer interfaces. PhD thesis, University of Illinois at Urbana-Champaign (1999)Google Scholar
  16. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Network of Plausible Inference. Morgan Kaufmann, San Francisco (1988)Google Scholar
  17. Perzanowski, D., Adams, W., Shulz, A.C.: Communicating with a semi-autonomous robot combining natural language and gesture,
  18. Perzanowski, D., Schultz, A., Adams, W.: Integrating natural language and gesture in a robotics domain. In: Proceedings of the IEEE International Symposium on Intelligent Control. National Institute of Standards and Technology, Gaithersburg, MD, pp. 247–252 (1998),
  19. Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. IEEE 77 (1989) Google Scholar
  20. Ramel, G.: Context Analysis by using Probabilistic Methods for the Humans-Robots Interaction. PhD thesis, Ecole Politechnique Fédérale de Lausanne (EPFL) (April 2006)Google Scholar
  21. Ramel, G., Tapus, A., Aspert, F., Siegwart, R.: Simple form recognition using bayesian programming. In: Proceedings of the 9th International Conference on Intelligent Autonomous Systems (IAS-9), Tokyo, Japan (2006)Google Scholar
  22. Russel, S.J., Norvig, P.: Artificial Intelligence-A Modern Approach. Prentice Hall, inc., Englewood Cliffs (1995)Google Scholar
  23. Tapus, A., Ramel, G., Dobler, L., Siegwart, R.: Topology learning and recognition using bayesian programming for mobile robot navigation. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan (2004)Google Scholar
  24. Torralba, A.: Contextual priming for object detection. Intl. J. Computer Vision 53(2), 153–167 (2003), CrossRefGoogle Scholar
  25. Torralba, A., Murphy, K., Freeman, W., Rubin, M.: Context-based vision system for place and object recognition. In: Intl. Conf. Computer Vision (2003),
  26. Torralba, A.B., Murphy, K.P., Freeman, W.T.: Contextual models for object detection using boosted random fields. In: NIPS (2004)Google Scholar
  27. Welch, G., Bishop, G.: An introduction to the kalman filter (April 2005),

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Guy Ramel
    • 1
  • Roland Siegwart
    • 2
  1. 1.Autonomous Systems Lab, Ecole Polytechnique Fédérale de Lausanne  
  2. 2.Autonomous Systems Lab, Institute of Robotics and Intelligent SystemsETH Zürich 

Personalised recommendations