Abstract
This paper describes a new approach for building 3D geometric maps using a laser rangefinder, a stereo camera system and a mathematical system the Conformal Geometric Algebra. The use of a known visual landmarks in the map helps to carry out a good localization of the robot. A machine learning technique is used for recognition of objects in the environment. These landmarks are found using the Viola and Jones algorithm and are represented with their position in the 3D virtual map.
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Zhang, L., Ghosh, B.K.: Line segment based map building and localization using 2d laser rangefinder. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 3, pp. 2538–2543 (2000)
Siadat, A., Kaske, A., Klausmann, S., Dufaut, M., Husson, R.: An optimized segmentation method for a 2d laser-scanner applied to mobile robot navigation. In: Proceedings of the 3rd IFAC Symposium on Intelligent Components and Instruments for Control Applications, pp. 153–158 (1997)
Bayro-Corrochano, E.: Robot perception and action using conformal geometry. In: Bayro-Corrochano, E. (ed.) The Handbook of Geometric Computing. Applications in Pattern Recognition, Computer Vision, Neurocomputing and Robotics, ch. 13, pp. 405–458. Springer, Heidelberg (2005)
Bayro-Corrochano, E., Daniilidis, K., Sommer, G.: Motor algebra for 3d kinematics: The case of the hand-eye calibration. Journal of Mathematical Imaging and Vision archive 13, 79–100 (2000)
Nguyen, V., Gächter, S., Martinelli, A., Tomatis, N., Siegwart, R.: A comparison of line extraction algorithms using 2d range data for indoor mobile robotics. Auton. Robots 23(2), 97–111 (2007)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, December 2001, pp. 511–518 (2001)
Di Stefano, L., Mattoccia, S., Tombari, F.: ZNCC-based template matching using bounded partial correlation. Pattern Recogn. Lett. 26(14) (2005)
Faugeras, O., et al.: Real-time correlation-based stereo: algorithm, implementation and applications. INRIA Technical Report no. 2013 (1993)
Azad, P., Gockel, T., Dillmann, R.: Computer Vision: Principles and Practice. Ed. Elektor Electronics (2008)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Ed. Cambridge University Press, Cambridge (2004)
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© 2009 Springer-Verlag Berlin Heidelberg
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Bernal-Marin, M., Bayro-Corrochano, E. (2009). Machine Learning and Geometric Technique for SLAM. In: Bayro-Corrochano, E., Eklundh, JO. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2009. Lecture Notes in Computer Science, vol 5856. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10268-4_100
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DOI: https://doi.org/10.1007/978-3-642-10268-4_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10267-7
Online ISBN: 978-3-642-10268-4
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