Abstract
Stereoscopic vision is an appropiate tool for building maps of the environment of a robot. When matching regions of the images, segmentation errors should be avoided. In this paper an algorithm to deal with errors in region matching is proposed, and the results in the presence of noise are analyzed. The selection of an appropiate similarity criterion to create the initial nodes in the graph-based matching process is very important for reducing the time of computation considerably. The experimental results show that the method is robust in the presence of noise.
This work was partially supported by grants TAP95-0710 (CICYT, Ministerio de Educación y Ciencia) and GV-2110/94 (Consellería de Educació y Ciència, Generalitat Valenciana).
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© 1998 Springer-Verlag Berlin Heidelberg
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López, M.A., Pla, F. (1998). Robust region-based stereo vision to build environment maps for robotics applications. In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds) Tasks and Methods in Applied Artificial Intelligence. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64574-8_422
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DOI: https://doi.org/10.1007/3-540-64574-8_422
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