Summary
In this paper we consider the problem of servoing wheeled vehicles in an indoor, initially unknown environment. The proposed approach relies on a hybrid (metric and topological) map built on visual cues. Navigation is planned using topological information to trace a path through viapoints that can be robustly performed by visual servoing control to accurately reach the goal positions.
A map of an unknown environment is built as a collection of images taken by an exploratory robot. Images represent nodes in a navigation graph, in which edges represent feasible paths that the robot can execute by visual servoing. Metric and topological information are stored in a hybrid map, which can be shared and cooperatively updated in real time by groups of robots. The merit of the proposed approach is to combine the accuracy of visual servoing methods with a reliable representation of an unknown environment. As a result, the method provides purely visual-based solutions to two of the most relevant problems involved respectively in the field of localization, that is the kidnapped robot problem, and in the field of mapping, that is the closed path detection problem. Experimental results on a laboratory setup are reported, showing the practicality of the proposed approach.
Support from EC Contract IST-2004-004536 (IP RUNES) and IST-2004-511368 (N.o.E. HYCON).
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Bicchi, A., Lorussi, F., Murrieri, P., Scordio, V.G.: MISTRAL - Methodologies and Integration of Subsystems and Technologies for Robotic Architectures and Locomotion. In: On The Problem of Simultaneous Localization, Map Building and Servoing of Autonomous Vehicles. STAR - Springer Tracts in Advanced Robotics, pp. 223–239. Springer, Heidelberg (2004)
Smith, R., Self, M., Cheeseman, P.: Estimating uncertain spatial relationships in robotics. In: Wilfong, G.T., Cox, I.J. (eds.) Autonomous Robot Vehnicles, vol. 5, pp. 167–193. Springer, Heidelberg (1990)
Crowley, J.: World modeling and position estimation for a mobile robot using ultrasonic ranging. In: Proc. IEEE Int. Conf. on Robotics and Automation, Scottsdale, AZ, USA, vol. 2, pp. 674–680 (May 1989)
Se, S., Lowe, D., Little, J.: Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. International Journal of Robotics Research 21, 735–758 (2002)
Karlsson, N., DiBernardo, E., Ostrowski, J., Goncalves, L., Pirjanian, P., Munich, M.E.: The vSLAM algorithm for robust localization and mapping. In: Proc. IEEE Int. Conf. on Robotics and Automation, Barcelona, Spain, pp. 24–29 (April 2005)
Murrieri, P., Fontanelli, D., Bicchi, A.: A hybrid-control approach to the parking problem of a wheeled vehicle using limited view-angle visual feedback. International Journal of Robotics Research 23(4–5), 437–448 (2004)
Dissanayake, M.W.M.G., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A solution to the simulataneous localization and map building (SLAM) problem. IEEE Transactions on Robotics and Automation 17(3), 229–241 (2001)
Danesi, A., Fontanelli, D., Murrieri, P., Scordio, V.G., Bicchi, A.: Cooperative simultaneous localization and map building for servoing. In: Proceedings of Multiagent Robotic Systems Trends and Industrial Application - Robocup 2003 Conference, Padova (July 2003)
Kosecka, J., Li, F.: Vision based topological Markov localization. In: Proc. IEEE Int. Conf. on Robotics and Automation, pp. 100–105 (2004)
Simhon, S., Dudek, G.: A global topological map formed by local metric maps. In: Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 1708–1714 (October 1998)
Tomasi, C., Kanade, T.: Detection and tracking of point features. Carnegie Mellon University, Tech. Rep. CMU-CS-91-132 (April 1991)
Thrun, S., Bücken, A.: Integrating grid-based and topological maps for mobile robot navigation. In: Proc. of the AAAI Thirteenth National Conf. on Artificial Intelligence, Portland, Oregon (1996)
Thrun, S., Koller, D., Ghahmarani, Z., DurrantWhyte, H.: SLAM Updates require constant time. School of Computer Science, Carnegie Mellon University, Tech. Rep. (2002)
Leonard, J., Rikoski, R., Newman, P., Bosse, M.: Mapping partially observable features from multiple uncertain vantage points. International Journal of Robotics Research 21(11), 943–975 (2002)
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Danesi, A., Fontanelli, D., Bicchi, A. (2008). Visual Servoing on Image Maps. In: Khatib, O., Kumar, V., Rus, D. (eds) Experimental Robotics. Springer Tracts in Advanced Robotics, vol 39. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77457-0_26
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DOI: https://doi.org/10.1007/978-3-540-77457-0_26
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