Abstract
In previous work, we proposed a unique landmark-based map learning method for mobile robots based on the “co-visibility” information i.e., very coarse qualitative information on “whether two objects are visible together or not”. In this paper, we introduce two major enhancements to this method: (1) automatic optimization of distance estimation function, and (2) weighting of observation information based on reliability. Simulation results show that these enhancements improve the performance of this proposed method dramatically, not only in the qualitative accuracy measure, but also in the quantitative measure.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Borg, I., Groenen, P.: Modern Multidimensional Scaling: Theory and Applications. Springer, Heidelberg (1997)
Castellanos, J.A., Montiel, J.M.M., Neira, J., Tardos, J.D.: The SPmap: A probabilistic framework for simultaneous localization and map building. IEEE Transactions on Robotics and Automation 15(5), 948–953 (1999)
Cox, T., Cox, M.: Multidimensional Scaling. Chapman & Hall/Crc, Boca Raton (2001)
Feder, H., Leonard, J., Smith, C.: Adaptive mobile robot navigation and mapping. International Journal of Robotics Research 18(7), 650–668 (1999)
Kuipers, B.: The spatial semantic hierarchy. Artificial Intelligence 119, 191–233 (2000)
Levitt, T., Lawton, D.: Qualitative navigation for mobile robots. Artificial Intelligence 44(3), 305–361 (1990)
Mataric, M.: Integration of representation into goal-driven behavior-based robots. IEEE Transactions on Robotics and Automation 8(3), 304–312 (1992)
Moravec, P., Elfes, A.: High resolution maps from wide angle sonar. In: Proc. IEEE Int. Conf. Robotics and Automation, pp. 116–121 (1985)
Schlieder, C.: Representing visible locations for qualitative navigation. In: Carret’e, N.P., Singh, M.G. (eds.) Qualitative Reasoning and Decision Technologies, pp. 523–532 (1993)
Shatkay, H., Kaelbling, L.: Learning topological maps with weak local odometric information. In: Proceedings of IJCAI 1997, pp. 920–927 (1997)
Sogo, T., Ishiguro, H., Ishida, T.: Acquisition and propagation of spatial constraints based on qualitative information. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(3), 268–278 (2001)
Thrun, S.: Learning metric-topological maps maps for indoor mobile robot navigation. Artificial Intelligence 99(1), 21–71 (1998)
Thrun, S., Fox, D., Burgard, W.: A probabilistic approach to concurrent mapping and localization for mobile robots. Machine Learning 31, 29–53 (1998)
Yairi, T., Hirama, K., Hori, K.: Fast and simple topological map construction based on cooccurrence frequency of landmark observation. In: Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2001), pp. 1263–1268 (2001)
Yairi, T., Hori, K.: Qualitative map learning based on co-visibility of objects. In: Proceedings of IJCAI 2003, pp. 183–188 (2003)
Young, G., Householder, A.: Discussion of a set of points in terms of their mutual distances. Psychometrika 3, 19–22 (1938)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yairi, T. (2004). Covisibility-Based Map Learning Method for Mobile Robots. In: Zhang, C., W. Guesgen, H., Yeap, WK. (eds) PRICAI 2004: Trends in Artificial Intelligence. PRICAI 2004. Lecture Notes in Computer Science(), vol 3157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28633-2_74
Download citation
DOI: https://doi.org/10.1007/978-3-540-28633-2_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22817-2
Online ISBN: 978-3-540-28633-2
eBook Packages: Springer Book Archive