Semantic Place Labeling Method

• Autorzy: Siemiatkowska B. , Harasymowicz-Boggio B. , Chechlinski Ł.

Identyfikatory

DOI

10.14313/JAMRIS_1-2015/4

• Języki publikacji: EN

Abstrakty

EN

The paper presents a method of semantic localization of a mobile robot. The robot is equipped with a Sick laser finder and a Kinect sensor. The simplest source of informa tion about an environment is a scan obtained by the range sensor. The polygonal approximation of an observed area is performed. The shape of the polygon allows us to distinguish corridors from other places using a simple rule based system. During the next step rooms are classified based on objects which have been recognized. Each object votes for a set of classes of rooms. In a real environment we deal with uncertainty. Usually probabilistic theory is used to solve the problem but it is not capable of capturing subjective uncertainty. In our approach instead of the classic Bayesian method we proposed to perform classification using Dempster-Shafer theory (DST), which can be regarded as a generalization of the Bayesian theory and is able to deal with subjective uncertainty. The experiments performed in real office environment proved the efficiency of our approach.

Słowa kluczowe

EN

mapping; classification; Dempster-Shafer theory

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2015
• Tom: Vol. 9, No. 1
• Strony: 28--33
• Opis fizyczny: Bibliogr. 25 poz., rys.

Twórcy

autor

Siemiatkowska B.

• Warsaw University of Technology, (22) 2348647, www: Faculty of Mechatronics

autor

Harasymowicz-Boggio B.

• Warsaw University of Technology, (22) 2348647, www: Faculty of Mechatronics

autor

Chechlinski Ł.

• Warsaw University of Technology, (22) 2348647, www: Faculty of Mechatronics

Bibliografia

• [1] “http://www.ros.org”, 2013.
• [2] A. Borkowski, B. Siemia̧tkowska, and J. Szklarski, “Towards semantic navigation in mobile robotics”, Lecture Notes in Computer Science, vol. 5765, 2010, 719–748.
• [3] P. Buschka and A. Saffiotti, “A virtual sensor for room detection”. In: Intelligent Robots and Systems (IROS), 2002, 637–642.
• [4] F. Dellaert, D. Fox, W. Burgard, and S. Thrun, “Monte Carlo localization for mobile robots”. In: IEEE Int. Conf. on Robotics & Automation (ICRA), 1998.
• [5] A. Dubrawski and B. Siemiatkowska, “A method for tracking pose of a mobile robot equipped with a scanning laser range finder”. In: ICRA’98, 1998, 2518–2523.
• [6] D. Fox, “Adapting the sample size in particle filters through KLD-sampling”, International Journal of Robotics Research, vol. 22, no. 12, 2003, 985–1003.
• [7] D. Fox, W. Burgard, F. Deallaert, and S. Thurn, “Monte-Carlo localization: Efficient position estimation for mobile robots”, National Conference on Artif􀏔icial Inteligence, 1999, 107–116.
• [8] M. Grewal and A. Andrews, “Kalman iltering: Theory and practice using MATLAB”, John Wiley and Sons, 2001.
• [9] B. Harasymowicz-Boggio and B. Siemia̧tkowska, “Object classification with metric and semantic inference”. In: Mobile Robots (ECMR), 2013 European Conference on, 2013, 186–191.
• [10] B. Harasymowicz-Boggio and B. Siemia̧tkowska, “Object classification using Dempster-Shafer theory”. In: R. Jablonski and T. Brezina, eds.,Mechatronics 2013: Recent Technological and Scientific Advances, 2014.
• [11] B. Harasymowicz-Boggio and B. Siemia̧tkowska,“Using ignorance in 3D scene understanding”,Mathematical Problems in Engineering, 2014.
• [12] S. Koenig and R. Simmons. “Xavier: A robot navigation architecture based on partially observable Markov decision process models”, 1998.
• [13] O. M. Mozos, R. Triebel, P. Jensfelt, A. Rottman, and W. Burgard, “Supervised semantic labeling of places using information extracted from sensor data”, Robotics and Autonomous Systems, vol. 5, 2007, 392–402.
• [14] A. Oliva and A. Torralba, “Modeling the shape of the scene: A holistic representation of the spatial envelope”, Int. J. Comput. Vision, vol. 42, no. 3, 2001, 145–175.
• [15] C. Olson, “Probabilistic selflocalization for mobile robots”, IEEE Transaction on Robotics and Automation, vol. 16, no. 1, 2000, 55–66.
• [16] S. Oore, G. Hinton, and G. Dudek. “A mobile robot that learns its place”, 1997.
• [17] A. Ranganathan, E. Menegatti, and F. Dellaert, “Bayesian inference in the space of topological maps”, TRO, vol. 22, no. 1, 2006, 92 – 107.
• [18] I. Rekleitis, “A particle filter tutorial for mobile robot localization”, Raport, Univerity Montreal, 2004.
• [19] L. W. Renninger and J. Malik, “When is scene identification just texture recognition?”, Vision Research, vol. 44, no. 19, 2004, 2301–2311.
• [20] R. B. Rusu, J. Bandouch, F. Meier, I. Essa, and M. Beetz, “Human Action Recognition using Global Point Feature Histograms and Action Shapes”, Advanced Robotics Journal, Robotics Society of Japan (RSJ), 2009.
• [21] B. Siemia̧tkowska, J. Szklarski, and M. Gnatowski, “Mobile robot navigation with the use of semantic map constructed from 3D laser range scans”, Control and Cybernetics, vol. 40, no. 2, 2011, 437–453.
• [22] M. M. Ullah, A. Pronobis, B. Caputo, J. Luo, P. Jensfelt, and H. I. Christensen, “Towards robust place recognition for robot localization”. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA08), 2008.
• [23] S. Vasudevan and R. Siegwart, “Bayesian space conceptualization and place classification for semantic maps in mobile robotics”, Robot. Auton. Syst., vol. 56, no. 6, 2008, 522–537.
• [24] M. Weigl, B. Siemiatkowska, K. A. Sikorski, and A. Borkowski, “Grid-based mapping for autonomous mobile robot”, Robotics and Autonomous Systems, 1993, 13–21.
• [25] J. Weingarten and R. Siegwart, “EKF-based 3D SLAM for structured environment reconstruction”. In: Proceedings of IROS, 2005.