Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Beal, R., Jackson, T.: Neural computing: an introduction, Adam Hilger, Bristol, Philadelphia and New York (1990)
Becker, A., Naïm, P.: Les réseaux bayésiens, Eyrolles, Paris (1999)
Caplat, G.: Modélisation cognitive et résolution de problèmes. Press Polytechniques Universitaires Romandes, CH-1015 Lausanne (2002)
Davalo, É., Naïm, P.: Des réseaux de neurones, Eyrolles, Paris (1990)
Delessert, A.: Introduction à la logique. Press Polytechniques Universitaires Romandes, CH-1015 Lausanne (1988)
Freeman, J.A., Skapura, D.M.: Neural Networks: Algorithms, Applications and Programming Techniques. Addison-Wesley, Reading (1991)
Jensen, B., Ramel, G., Siegwart, R.: Detecting semi-static objects with a laser scanner. In: Autonome Mobile Systeme (AMS 2003) (2003)
Jensen, F.V.B.: An introduction to Bayesian Networks. UCL Press (1996)
Kortenkamp, D., Huber, E., Bonasso, R.P.: Recognizing and interpreting gestures on a mobile robot. In: AAAI/IAAI, vol. 2, pp. 915–921 (1996), citeseer.nj.nec.com/kortenkamp96recognizing.html
Lauritzen, S.L.: Graphical Models. Clarendon press, Oxford (1996)
Lebeltel, O.: Programmation Bayésienne des robots. PhD thesis, Institut National Polytechnique de Grenoble (1999)
Lebeltel, O., Bessière, P., Diard, J., Mazer, E.: Bayesian robot programming. Advanced Robotics 16(1), 49–79 (2004)
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)
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)
Pavlovic, V.I.: Dynamique bayesian networks for information fusion with applications to human-computer interfaces. PhD thesis, University of Illinois at Urbana-Champaign (1999)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Network of Plausible Inference. Morgan Kaufmann, San Francisco (1988)
Perzanowski, D., Adams, W., Shulz, A.C.: Communicating with a semi-autonomous robot combining natural language and gesture, citeseer.nj.nec.com/445213.html
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), citeseer.nj.nec.com/perzanowski98integrating.html
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. IEEE 77 (1989)
Ramel, G.: Context Analysis by using Probabilistic Methods for the Humans-Robots Interaction. PhD thesis, Ecole Politechnique Fédérale de Lausanne (EPFL) (April 2006)
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)
Russel, S.J., Norvig, P.: Artificial Intelligence-A Modern Approach. Prentice Hall, inc., Englewood Cliffs (1995)
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)
Torralba, A.: Contextual priming for object detection. Intl. J. Computer Vision 53(2), 153–167 (2003), citeseer.ist.psu.edu/torralba03contextual.html
Torralba, A., Murphy, K., Freeman, W., Rubin, M.: Context-based vision system for place and object recognition. In: Intl. Conf. Computer Vision (2003), citeseer.comp.nus.edu.sg/571119.html
Torralba, A.B., Murphy, K.P., Freeman, W.T.: Contextual models for object detection using boosted random fields. In: NIPS (2004)
Welch, G., Bishop, G.: An introduction to the kalman filter (April 2005), http://www.cs.unc.edu/~welch/kalman/
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Ramel, G., Siegwart, R. (2008). Probabilistic Contextual Situation Analysis. In: Bessière, P., Laugier, C., Siegwart, R. (eds) Probabilistic Reasoning and Decision Making in Sensory-Motor Systems. Springer Tracts in Advanced Robotics, vol 46. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79007-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-540-79007-5_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79006-8
Online ISBN: 978-3-540-79007-5
eBook Packages: EngineeringEngineering (R0)