Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Localization is a fundamental problem of autonomous mobile robots. Localization is the determination of the position and orientation of a robot. Most localization systems are made up of several sensors and a map of the environment. Sophisticated localization systems can solve both the global location problem and the kid-napped robot problem. Global localization is the process of placing the robot into an unknown location within the map, and the robot should be able to locate itself within a relatively short period of time. The kidnapped robot problem is similar to global localization, as it is a test of how well the robot is able to recover after becoming lost. The robot is “teleported” to a new location, and the robot should again be able to determine its new location within a relatively short amount of time. CReSIS (Center for Remote Sensing of Ice Sheets) is developing autonomous robots in an effort to measure ice sheets characteristics in Greenland and Antarctica. These robots currently rely on differential GPS for localization and navigation. In order to survive for long periods of time in these environments, however, the robots need to be able to return to campsites in order to refuel and unload the data that has been acquired. In order to perform this task effectively and safely, a more elaborate system is required. A localization system that can recognize the dfferent locations of the campsites is the beginning of this process. The approach is to use a single camera for use in multiple types of large-scale environments, indoors and outdoors using a topological map. The system described uses an appearance-based approach for recognizing the different locations. The appearance-based methods attempt to recognize the appearance of a scene rather than specific objects. Several different types of features are tested including histograms, eigenimages, and Hu Moments. Using these simple features, the system is able to determine its location within the map at 95% accuracy.
Rocznik
Tom
Strony
68--84
Opis fizyczny
Bibliogr. 31 poz., rys.
Twórcy
autor
autor
- Department of Mathematics and Computer Science, Elizabeth City State University, agah@ku.edu
Bibliografia
- [1] Akers E.L., Agah A., „Large-scale localization using only a camera”.In Proceedings of the 2008 IEEE International Conference on Technologies for Practical Robotics Applications (TePRA), Woburn, Massachusetts, 2008, pp. 25-30.
- [2] Akers E.L., Harmon H.P., Stansbury R.S., Agah A., „Design, fabrication, and evaluation of a mobile robot for polar environments”. In:Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Anchorage, Alaska, 2004, pp. 109-112.
- [3] Akers E.L., Stansbury R.S., Agah A., „Long-term survival of polar mobile robots”. In: Proceedings of the 4 International Conference on Computing, Communications and Control Technologies (CCCT), Orlando, Florida, vol. II, 2006, pp. 329-333.
- [4] Akers E.L., Stansbury R.S., Agah A.,, Akins T.L., „Mobile robots for harsh environments: lessons learned from field experiments”. In: Proceedings of the 11 International Symposium on Robotics and Applications (ISORA), Budapest, Hungary. 2006, p. 16.
- [5] Blaer P., Allen P. K., „Topological mobile robot localization using fast vision techniques”. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Washington D.C., 2002, pp. 1031-1036.
- [6] Bouman C.A., „Cluster: An unsupervised algorithm for modeling Gaussian mixtures”, 1997. Available from http://www.ece.purdue.edu/~bouman.
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- [8] Burgard W., Derr A., Fox D., Cremers A., „Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approach”. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '98), Victoria, BC, Canada, 1998, pp. 730-735.
- [9] CReSIS. Center for Remote Sensing of Ice Sheets, 2007. URL: http://www. cresis.ku.edu.
- [10] DARPA grand challenge, 2006. http://www.darpa.mil/grandchallenge/index.asp.
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- [12] Duda R.O., Hart P.E., Stork D.G. Pattern Classification. Wiley-Interscience, 2 edition, 2000.
- [13] Gifford C.M., Agah A., „Precise formation of multi-robot systems”. In: Proceedings of the IEEE International Conference on Systems of Systems Engineering (SoSE), San Antonio, Texas, 2007, no. 105, pp. 1-6.
- [14] Gonzalez R.C., Woods R.E. Digital Image Processing, New Jersey: Prentice Hall, 2 edition, 2002.
- [15] Hu M.-K., „Visual pattern recognition by moment invariants” Information Theory, IEEE Transactions on, 1962, vol. 8, no. 2, pp. 178-187.
- [16] Jogan M., Leonardis A., Wildenauer H., Bischof H., „Mobile robot localization under varying illumination”. In: Proceedings of the International Conference on Pattern Recognition (ICPR), Quebec, Canada, 2002, pp. 1-4.
- [17] Kosecka J., Li F., „Vision based topological markov localization”. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2004, vol. 2, pp. 1481-1486.
- [18] Kr¨ose B., Vlassis N., Bunschoten R., Motomura Y., „A probabilistic model for appearance-based robot localization”. Image and Vision Computing, 2001, vol. 19, no. 6, pp. 381-391.
- [19] KU PRISM Team. Polar Radar for Ice Sheet Measurements, 2004. URL: http://www.ku-prism.org.
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- [26] Russell S.J., Norvig P., „Artificial Intelligence: A Modern Approach”. Englewood Clis, New Jersey: Pearson Education, 2003.
- [27] Sim R., Dudek, G., „Comparing image-based localization methods”. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI), Acapulco, Mexico, 2003, pp. 1560-1562.
- [28] Thrun S., Bennewitz M., Burgard W., Cremers A., Dellaert F., Fox D., Haehnel D., Rosenberg C., Roy N., Schulte J., Schulz D., „Minerva: A second generation mobile tour-guide robot”. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 1999.
- [29] Thrun S., Gutmann J.-S., Fox D., Burgard W., Kuipers B., „Integrating topological and metric maps for mobile robot navigation: A statistical approach”. In: Proceedings of the National Conference on Artificial Intelligence (AAAI), Orlando, FL, 1998, pp. 989-996.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ7-0012-0008