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Vision-Based Mobile Robot Navigation

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This paper presents a vision-based navigation system for mobile robots. It enables the robot to build a map of its environment, localize efficiently itself without use of any artificial markers or other modifications, and navigate without colliding with obstacles. The Simultaneous Localization And Mapping (SLAM) procedure builds a global representation of the environment based on several size limited local maps built using the approach introduced by Davison [1]. Two methods for global map are presented; the first method consists in transforming each local map into a global frame before to start building a new local map. While in the second method, the global map consists only in a set of robot positions where new local maps are started (i.e. the base references of the local maps). In both methods, the base frame for the global map is the robot position at instant . Based on the estimated map and its global position, the robot can find a path and navigate without colliding with obstacles to reach a goal defined the user. The moving objects in the scene are detected and their motion is estimated using a combination of Gaussian Mixture Model (GMM) background subtraction approach and a Maximum a Posteriori Probability Markov Random Field (MAP-MRF) framework [2]. Experimental results in real scenes are presented to illustrate the effectiveness of the proposed method.
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Bibliografia
  • [1] A. 3. Davison, I. D. Reid, N. D. Molton, O. Stasse, "Mono SLAM: Real-Time Single Camera SLAM", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, issue 6, 3une 2007, pp.10521067.
  • [2] S.A. Berrabah, G. De Cubber, V. Enescu, H. Sahli, "MRF-Based Foreground Detection in Image Sequences from a Moving Camera". In: IEEE International Conference on Image Processing (KIP 2006), Atlanta, GA, USA, Oct. 2006, pp.1125-1128.
  • [3] A. 3. Davison, Y. G. Cid, N. Kita, "Real-time 3D SLAM with wide-angle vision". In: Intelligent Autonomous Vehicles, Lisboa-Portugal, Jul. 2004.
  • [4] 3. Folkesson, P. Jensfelt, H. Christensen, "Graphical SLAM using vision and the measurement subspace". In: IEEE/JRS -Intl Conf. on Intelligent Robotics and Systems (IROS), Edmonton-Canada, Aug 2005.
  • [5] F. Andrade-Cetto, A. Sanfelin, "Concurrent Map Building and Localization with landmark validation". In: 16th International Conference on Pattern Recognition ICP/?/02,2002,vol.2.
  • [6] J. W. Fenwick, P. M. Newman, 3. 3. Leonard, "Cooperative Concurrent Mapping and Localization". In: Proceedings of the 2002 IEEE International Conference on Robotics and Automation, May 2002, Washington, USA, pp. 1810-1817.
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  • [9]  M. W. M. Gamini Dissanayake, P. Newman, S. Clark, H. F.,Durrant-Whyte, M. Csorba, "A Solution to the Simulta-neous Localization and Map Building (SLAM) Problem", IEEE Transactions on Robotic and Automation, 2001, vol. 17, no.3,pp.229-241.
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  • [12] M.S. Kim, S.G. Hong, 3.3. Lee, "Self-Learning Fuzzy Logic Controller using Q-Learning", Journal of Advanced Computational Intelligence and Intelligent Informatics, vol.4, no. 5, 2000, pp. 349-354.
  • [13] A. Gonzalez, R. Perez, A Learning System of Fuzzy Control Rules Based on Genetic Algorithms, Genetic Algorithms and Soft Computing, Studies in Fuzziness and Soft Computing, vol. 8, Physica-Verlag, September 1996, pp. 202-225.
  • [14] M. Henning, S. Vinoski, Advanced corba programming with C++, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1999.
  • [15] E. Colon, H. Sahli, and Y. Baudoin, "Coroba, a multi mobile robot control and simulation framework", Special Issue on "Software Development and Integration in Robotics" of the International Journal on Advanced Robotics, vol. 3, 2006, no. l, pp. 73-78.
  • [16] Thomas Geerinck, Eric Colon, Sid Ahmed Berrabah, and Kenny Cauwerts, "Tele-robot with shared autonomy: Distributed navigation development framework", Integrated Computer-Aided Engineering (ICAE), vol. 13, no.4, 2006,pp.329-346.
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Bibliografia
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bwmeta1.element.baztech-article-BUJ5-0021-0001
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