Classification of the indoor environment of a mobile robot using principal component analysis

• Autorzy: Yaqub T. , Katupitya J.
• Języki publikacji: EN

Abstrakty

EN

Large indoor environments of a mobile robot usually consist of different types of areas connected together. The structure of a corridor differs from a room, a main hall or laboratory. A method for online classification of these areas using a laser scanner is presented in this paper. This classification can reduce the search space of localization module to a great extent making the navigation system efficient. The intention was to make the classification of a sensor observation in a fast and real-time fashion and immediately on its arrival in the sensor frame. Our approach combines both the feature based and statistical approaches. We extract some vital features of lines and corners with attributes such as average length of lines and distance between corners from the raw laser data and classify the observation based on these features. Bootstrap method is used to get a robust correlation of features from training data and finally Principal Component Analysis (PCA) is used to model the environment. In PCA, the underlying assumption is that data is coming from a multivariate normal distribution. The use of bootstrap method makes it possible to use the observations data set which set, which is not necessarily normally distributed. This technique lifts up the normality assumption and reduces the computational cost further as compared to the PCA techniques based on raw sensor data and can be easily implemented in moderately complex indoor environment. The knowledge of the environment can also be up-dated in an adaptive fashion. Results of experimentation in a simulated hospital building under varying environmental conditions using a real-time robotic software Player/Stage are shown.

Słowa kluczowe

EN

mobile robot environment; PCA; classification; feature extraction; training data; bootstrap method

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2008
• Tom: Vol. 2, No. 2
• Strony: 44--53
• Opis fizyczny: Bibliogr. 20 poz., rys.

Twórcy

autor

Yaqub T.

autor

Katupitya J.

• School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, Australia,

Bibliografia

• [1] F. Pourraz, J.L. Crowley, "Use of eigenspace techniques for position estimation". In: International Workshop on Advanced Motion Control, AMC, 1998, pages 267 271.
• [2] J. L. Crowley, F. Wallner, B. Schiele, "Position estimation using principal components of range data", Robotics and Autonomous Systems, vol. 23, 1998, pp. 267-276.
• [3] M. Artac, "Mobile robot localisation with incremental pca". In: Proceedings of the Mediter-ranean Electrotechnical Conference-MELECON, vol. l, 2002, pp. 192-197.
• [4] M. Artac, M. Jogan, A.Leonardis, "Mobile robot locali-zation using an incre-mental eigenspace model". In: Proceedings - IEEE International Conference on Robotics andAutomation, vol. l, 2002, pp. 1025-1030.
• [5] M. Jogan, L. Ales, "Robust localization using an omni-directional appearance-based subspace model of environment", Robotics and Autonomous Systems, vol. 45, issue 1, 2003, pp. 51-72.
• [6] B. J. A. Krose, N. Vlassis, R. Bunschoten, Y. Motomura, "A probabilistic model for appearance-based robot localization", Image and Vision Computing, vol 19, April 2001, pp. 381-391.
• [7] M. Artac, M. Jogan, and A. Leonardis, "Mobile robot localization using an incremental eigenspace model". In: "Proceedings of the 2002 IEEE International Conference on Robotics and Automation, Washington D.C., May 2002, pp. 1025-1030.
• [8] N. Vlassis, B. Krose, "Robot environment modeling via principal component regression". In: Proceedings of the 1999 IEEERSJ International Conference on Intelligent Robots and Systems, volume 2,1999, pp. 677-682.
• [9] P. Weckesser, R. Dillmann, "Modeling unknown environments with a mobile robot", Robotics and Autonomous Systems, vol. 23, July 1998, pp. 293-300.
• [10] Yong-Ge Wu, Jing-Yu Yang, and Ke Liu, "Obstacle detection and environment modeling based on multisensor fusion for robot navigation", Artificial Intelligence in Engineering, vol.1O, issue 4, November 1996, pp. 323-333.
• [11] S. Rolfes, M-J. Rendas. "Statistical environment representation for navigation in natural environments". Robotics and Autonomous Systems, vol. 41, no. 2, 2002, pp.129-136.
• [12] 0. Gasos, A. Rosetti, "Uncertainty representation for mobile robots: Perception, modeling and navigation in unknown environments", Fuzzy Sets and Systems, vol. 107, issue l, October 1999, pp. 1-24.
• [13] S. Yamada, "Recognizing environments from action sequences using self-organizing maps", Applied Soft Computing, vol 4, February 2004, pp. 35-47.
• [14] B. Kuipers, P. Beeson, "Bootstrap learning for place recognition". In: `18th national conference on Artificial intelligence, Menlo Park, CA, USA, 2002. American Asso-ciation for Artificial Intelligence, pp. 174-180.
• [15] I.T. Joltife, Principal Component Analysis, Springer-Verlag, 1986.
• [16] T. Yaqub, J. Katupitiya, "Modelling the environment of a mobile robot using feature-based principal component analysis". In: IEEE interantional conference on Cybernetics, Intelligent Systems and Robotics, Automation and Mechatronics (IEEE CIS-RAM-06), Bangkok, Thailand, June 2006, pp. 1-6.
• [17] Y. Cang, J. Borenstein, "Characterization of a 2d laser scanner for mobile robot obstacle negotiation". In: Proceedings of the 2002 IEEE International Conference on Robotics and Automation, volume 3, Washington D.C., May 2002, pp. 2512-2518.
• [18] Tahir. Yaqub. and J. Katupitiya. Robot Motion and Control, chapter A Parametric Representation of the Environment of a Mobile Robot for Measurement Update in a Particle Filter, Springer-Verlag Ltd, London, June 2007.
• [19] B. P. Gerkey, R. T. Vaughan, A. Howard, "The player/ stage project: Tools for multi-robot and distributed sensor systems". In: Proceedings of the International Conference on Advanced Robotics (KAR 2003), Coimbra, Portugal, 2003, pp. 317-323.
• [20] B. Efron, R. J. Tibshirani, An Introduction to the Bootstrap, Chapman & Hall/CRC, May 1994.