Abstract
Dealing with methods of human-robot interaction and using a real mobile robot, stable methods for people detection and tracking are fundamental features of such a system and require information from different sensory. In this paper, we discuss a new approach for integrating several sensor modalities and we present a multimodal people detection and tracking system and its application using the different sensory systems of our mobile interaction robot Horos working in a real office environment. These include a laser-range-finder, a sonar system, and a fisheye-based omnidirectional camera. For each of these sensory information, a separate Gaussian probability distribution is generated to model the belief of the observation of a person. These probability distributions are further combined using a flexible probabilistic aggregation scheme. The main advantages of this approach are a simple integration of further sensory channels, even with different update frequencies and the usability in real-world environments. Finally, promising experimental results achieved in a real office environment will be presented.
This work is partially supported by TMWFK-Grant #B509-03007 to H.-M. Gross and a HWP-Grant to A. Scheidig.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Fröba, B., Külbeck, C.: Real-time face detection using edge-orientation matching. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 78–83. Springer, Heidelberg (2001)
Martin, C., Boehme, H.J., Gross, H.M.: Conception and realization of a multi-sensory interactive mobile office guide. In: IEEE Conference on Systems, Man and Cybernetics, pp. 5368–5373 (2004)
Simmons, R., Goldberg, D., Goode, A., Montemerlo, M., Roy, N., Sellner, B., Urmson, C., Schultz, A., Abramson, M., Adams, W., Atrash, A., Bugajska, M., Coblenz, M., MacMahon, M., Perzanowski, D., Horswill, I., Zubek, R., Kortenkamp, D., Wolfe, B., Milam, T., Maxwell, B.: Grace: An autonomouse robot for AAAI robot challenge. AAAI Magazine 24, 51–72 (2003)
Schulz, D., Burgard, W., Fox, D., Cremers, A.: Tracking multiple moving objects with a mobile robot. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 371–377 (2001)
Schulz, D., Burgard, W., Fox, D., Cremers, A.: People tracking with a mobile robot using sample-based joint probabilistic data association filters. International Journal of Robotics Research (IJRR) 22, 99–116 (2003)
Nakadai, K., Okuno, H.G., Kitano, H.: Auditory fovea based speech separation and its application to dialog system. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2002), vol. 2, pp. 1320–1325 (2002)
Siegwart, R., Arras, K.O., Bouabdallah, S., Burnier, D., Froidevaux, G., Greppin, X., Jensen, B., Lorotte, A., Mayor, L., Meisser, M., Philippsen, R., Piguet, R., Ramel, G., Terrien, G., Tomatis, N.: Robox at expo.02: A large scale installation of personal robots. Special issue on Socially Interactive Robots, Robotics and Autonomous Systems 42, 203–222 (2003)
Fritsch, J., Kleinehagenbrock, M., Lang, S., Fink, G., Sagerer, G.: Audiovisual person tracking with a mobile robot. In: Groen, F., Amato, N., Bonarini, A., Yoshida, E., Kröse, B. (eds.) Int. Conf. on Intelligent Autonomous Systems, pp. 898–906. IOS Press, Amsterdam (2004)
Fritsch, J., Kleinehagenbrock, M., Lang, S., Ploetz, T., Fink, G.A., Sagerer, G.: Multi-modal anchoring for human-robot-interaction. Robotics and Autonomous Systems, Special issue on Anchoring Symbols to Sensor Data in Single and Multiple Robot Systems 43, 133–147 (2003)
Wilhelm, T., Boehme, H.J., Gross, H.M.: A multi-modal system for tracking and analyzing faces on a mobile robot. In: Robotics and Autonomous Systems, vol. 48, pp. 31–40 (2004)
Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. International Journal on Computer Vision 29, 5–28 (1998)
MacCormick, J., Blake, A.: Probabilistic exclusion and partitioned sampling for multiple object tracking. International Journal on Computer Vision 39, 57–71 (2000)
Julier, S., Uhlmann, J.: A nondivergent estimation algorithm in the presence of unknown correlations. In: Proceedings of the 1997 American Control Conference, vol. 4, pp. 2369–2373. IEEE, Los Alamitos (1997)
Chen, L., Arambel, P., Mehra, R.: Estimation under unknown correlation: Covariance intersection revisited. IEEE Transactions on Automatic Control 47, 1879–1882 (2002)
Viola, P., Jones, M.: Fast and robust classication using asymmetric adaboost and a detector cascade. In: NIPS 2001, pp. 1311–1318 (2001)
Viola, P., Jones, M.: Robust real-time object detection. In: Proc. of IEEE Workshop on Statistical and Computational Theories of Vision (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schaffernicht, E., Martin, C., Scheidig, A., Gross, HM. (2005). A Probabilistic Multimodal Sensor Aggregation Scheme Applied for a Mobile Robot. In: Furbach, U. (eds) KI 2005: Advances in Artificial Intelligence. KI 2005. Lecture Notes in Computer Science(), vol 3698. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551263_26
Download citation
DOI: https://doi.org/10.1007/11551263_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28761-2
Online ISBN: 978-3-540-31818-7
eBook Packages: Computer ScienceComputer Science (R0)