Abstract
In this paper we present a novel approach to estimate the position of objects tracked by a team of robots. Moving objects are commonly modeled in an egocentric frame of reference, because this is sufficient for most robot tasks as following an object, and it is independent of the robots localization within its environment. But for multiple robots, to communicate and to cooperate the robots have to agree on an allocentric frame of reference. Instead of transforming egocentric models into allocentric ones by using self localization information, we will show how relations between different objects within the same camera image can be used as a basis for estimating an object’s position. The spacial relation of objects with respect to stationary objects yields several advantages: a) Errors in feature detections are correlated. The error of relative positions of objects within a single camera frame is comparably small. b) The information is independent of robot localization and odometry. c) Object relations can help to detect inconsistent sensor data. We present experimental evidence that shows how two non-localized robots are capable to infer the position of an object by communication on a RoboCup Four-Legged soccer field.
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References
Arkin, R.C.: Behavior-Based Robotics. MIT Press, Cambridge (1998)
Dietl, M., Gutmann, J., Nebel, B.: Cooperative sensing in dynamic environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2001), Maui, Hawaii (2001)
Fox, D., Burgard, W., Dellaert, F., Thrun, S.: Monte carlo localization: Efficient position estimation for mobile robots. In: Proceedings of the Sixteenth National Conference on Artificial Intelligence and Eleventh Conference on Innovative Applications of Artificial Intelligence (AAAI), pp. 343–349. The AAAI Press/The MIT Press (1999)
Göhring, D., Burkhard, H.-D.: Multi robot object tracking and self localization using visual percept relations. In: Proceedings of IEEE/RSJ International Conference of Intelligent Robots and Systems (IROS), pp. 31–36. IEEE (2006)
Gutmann, J.-S., Fox, D.: An experimental comparison of localization methods continued. In: Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2002)
Kaplan, K., Celik, B., Mericli, T., Mericli, C., Akin, L.: Practical extensions to vision-based monte carlo localization methods for robot soccer domain. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020. Springer, Heidelberg (to appear, 2006)
Kwok, C., Fox, D.: Map-based multiple model tracking of a moving object. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS (LNAI), vol. 3276, pp. 18–33. Springer, Heidelberg (2005)
Lenser, S., Bruce, J., Veloso, M.: CMPack: A complete software system for autonomous legged soccer robots. In: AGENTS 2001: Proceedings of the fifth international conference on Autonomous agents, pp. 204–211. ACM Press (2001)
Montemerlo, M., Thrun, S.: Simultaneous localization and mapping with unknown data association using FastSLAM. In: Proceedings of the 2003 IEEE International Conference on Robotics and Automation (ICRA), pp. 1985–1991. IEEE (2003)
Nguyen, V., Martinelli, A., Tomatis, N., Siegwart, R.: A comparison of line extraction algorithms using 2d laser rangefinder for indoor mobile robotics. In: Proceedings of the IEEE/RSJ Intenational Conference on Intelligent Robots and Systems, IROS, Edmonton, Canada. IEEE (2005)
Schmitt, T., Hanek, R., Beetz, M., Buck, S., Radig, B.: Cooperative probabilistic state estimation for vision-based autonomous mobile robots. IEEE Transactions on Robotics and Automation 18(5), 670–684 (2002)
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)
Thrun, S., Fox, D., Burgard, W.: Monte carlo localization with mixture proposal distribution. In: Proceedings of the National Conference on Artificial Intelligence (AAAI), pp. 859–865 (2000)
Wagner, T., Huebner, K.: An egocentric qualitative spatial knowledge representation based on ordering information for physical robot navigation. In: ECAI 2004, Workshop on Issues in Designing Physical Agents for Dynamic Real-Time Environments (2004)
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Göhring, D. (2008). Cooperative Object Localization Using Line-Based Percept Communication. In: Visser, U., Ribeiro, F., Ohashi, T., Dellaert, F. (eds) RoboCup 2007: Robot Soccer World Cup XI. RoboCup 2007. Lecture Notes in Computer Science(), vol 5001. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68847-1_5
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DOI: https://doi.org/10.1007/978-3-540-68847-1_5
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