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Mobile robot navigation with the use of semantic map constructed from 3D laser range scans

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We describe a system allowing a mobile robot equipped with a 3D laser range finder to navigate in the indoor and outdoor environment. A global map of the environment is constructed, and the particle filter algorithm is used in order to accurately determine the position of the robot. Based on data from the laser only, the robot is able to recognize certain classes of objects like a floor, a door, a washbasin, or a wastebasket, and places like corridors or rooms. For complex objects, the recognition process is based on the Haar feature identification. When an object is detected and identified, its position is associated with the appropriate place in the global map, making it possible to give orders to the robot with the use of semantic labels, e.g., "go to the nearest wastebasket ". The obstaclefree path is generated using a Cellular Neural Network, accounting for travel costs with distance or ground quality. This path planning method is fast and in comparison with the potential field method it does not suffer from the local minima problem. We present some results of experiments performed in a real indoor environment.
Słowa kluczowe
Rocznik
Strony
437--453
Opis fizyczny
Bibliogr. 35 poz., il.
Twórcy
autor
autor
  • Warsaw University of Technology, Warsaw, Poland
Bibliografia
  • Azarm, K. and Schmidt, G. (1996) A Decentralized Approach for the Conflict-free Motion of Multiple Mobile Robots. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 1667-1674.
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  • Bennewitz,M., Burgard, W. and Thrun, S. (2000) Optimizing Schedule for prioritized path planning of multi-robot systems. In: Proc. of the IEEE International Conference on Robotics & Automation (ICRA). IEEE, 1, 271-276.
  • Besl, P. and McKay, N. (1992) A method for Registration of 3-D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 239-256.
  • Borkowski,A., Siemiątkowska,B. and Szklarski, J. (2010) Towards Semantic Navigation in Mobile Robotics. In: G. Engels, C. Lewerentz, W. Schäfer, A. Schürr, and B. Westfechtel, eds., Graph Transformations and Model-Driven Engineering. Essays Dedicated to Manfred Nagl. LNCS 5765, Springer, 730-759.
  • Buckley, S.J. (1989) Fast Motion Planning for Multiple Moving Robots. In: Proc. of the IEEE International Conference on Robotics and Automation (ICRA). IEEE, 1419-1424.
  • Chen, Y. and Medioni, G. (1991) Object Modeling by Registration of Multiple Range Images. In: Proc. IEEE Conf. on Robotics and Automation. IEEE, 616-625.
  • Chu, H. and Eimaraghy, A. (1992) Real-timeMulti-robot Path Planner Based on a Heuristic Approach. In: Proc. of the IEEE International Conference on Robotics & Automation (ICRA). IEEE, 1, 475-478.
  • Chua, L. and Roska, T. (1993) The CNN paradigm. IEEE Transactions on Circuit Systems, 40, 147-156.
  • Chua, L. and Young, L. (1988) Cellular Neural Network. IEEE Transactions on Circuit Systems, 35, 1271-1290.
  • Duda, R.O. and Hart, P.E. (1972) Use of the Hough Transformation to Detect Lines and Curves in Pictures. Communications of the ACM, 15(1), 11-15.
  • Elfes, A. (1987) Sonar-based Real-world Mapping and Navigation. IEEE Journal of Robotics and Automation, 3, 249-265.
  • Fox, D. (2003) Adapting the Sample Size in Particle Filters through KLDsampling. International Journal of Robotics Research, 22(12), 985-1003.
  • Fox, D., Burgard, W., Deallert, F. and Thrun, S. (1999) Monte Carlo Localization: Efficient Position Estimation for Mobile Robots. Proc. of the Sixteenth National Conference on Artificial Intelligence (AAAI-99). AAAI, 343-349.
  • Gnatowski,M., Siemiątkowska, B. and Szklarski, J. (2010) Extraction of semantic information from the 3D laser range finder. In: W. Schiehlen and V. Parenti-Castelli, eds., ROMANSY 18. Robot Design, Dynamics and Control. CISM 524, Springer, 383-390.
  • Grewal, M.S. and Andrews, A.P. (2001) Kalman Filtering: Theory and Practice Using MATLAB. John Wiley and Sons.
  • Gutmann, J.S., Burgard, W., Fox, D. and Konolige, K. (1998) Experimental Comparison of Localization Methods. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’98). IEEE.
  • Kröse,B., Booij,O. and Zivkovic,Z. (2007) A Geometrically Constrained Image Similarity Measure for Visual Mapping, Localization and Navigation. In: W. Burgard and H.M. Gross, eds., Proc. of the 3rd European Conference on Mobile Robots, EMCR 2007. http://ecmr07.informatik. Uni-freiburg.de/proceedings/ECMR07_0072.pdf.
  • Latombe, J.C. (1992) Robot Motion Planning. Kluwer Academic Publishers, MA Boston.
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  • Mozos,O.M., Triebel,R., Jensfelt,P., Rottman,A. and Burgard,W. (2007) Supervised Semantic Labeling of Places Using Information Extracted From Sensor Data. Robotics and Autonomous Systems, 5, 392-402.
  • Nüchter, A., Lingemann, K., Hertzberg, J. and Surmann, H. (2005) Accurate Object Localization in 3D Laser Range Scans. In: In Proceedings of the 12th IEEE International Conference on Advanced Robotics (ICAR ’05). IEEE, 665-672.
  • Nüchter, A., Surmann, H. and Hertzberg, J. (2004) Automatic Classification of Objects in 3D Laser Range Scans. In: Proc. 8th Conf. On Intelligent Autonomous Systems. IOS Press, 963-970.
  • Olson,C. (2000) Probabilistic Selflocalization for Mobile Robots. IEEE Transaction on Robotics and Automation, 16(1), 55-66.
  • Pfingsthorn, M., Slamet, B. and Visser, A. (2007) A Scalable Hybrid Multi-robot Slam Method for Highly Detailed Maps. In: Proceedings of the 11th RoboCup International Symposium. IEEE, 457-464.
  • Rekleitis, I.M. (2004) A Particle Filter Tutorial for Mobile Robot Localization. Technical report, University of Montreal.
  • Remolina, E. and Kuipers, B. (2004) Towards a General Theory of Topological Maps. Artificial Intelligence, 152(1), 47 - 104.
  • Rusu, R.B., Martonand, Z.C., Blodow, N., Dolha, M. and Beetz, M. (2008) Towards 3d Point Cloud Based Object Maps for Household Environment. Journal of Robotics and Autonomous Systems, 56, 927-941.
  • Siemiątkowska, B. and Dubrawski, A. (1999) Global Map Building and Path Planning for Mobile Robots. LNCS 1424, Springer, 68-74.
  • Siemiątkowska, B., Szklarski, J., Gnatowski, M. and Borkowski, A. (2009) Towards Semantic Navigation System. In: M. Klopotek, A. Przepiorkowski, S. Wierzchon and K. Trojanowski, eds., Recent Advances in Intelligent Information Systems. Warsaw, Exit, 711-720.
  • Thrun, S., Burgard, W. and Fox, D. (2005) Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press.
  • Weingarten, J. and Siegwart, R. (2005) EKF-based 3D SLAM for Structured Environment Reconstruction. In: Proc. of IROS 2005. IEEE, 3834-3839.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BATC-0008-0011
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