/npsi/ctrl?lang=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?lang=fr Access and use of this website and the material on it are subject to the Terms and Conditions set forth at http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. For the publisher's version, please access the DOI link below./ Pour consulter la version de l'éditeur, utilisez le lien DOI ci-dessous.http://dx.doi.org/10.1061/(ASCE) IS.1943-555X.0000097 Journal of Infrastructure Systems, 19, 1, pp. 108-119, 2012-02-06 Performance of ductile iron pipes. I : characterization of external corrosion patterns Kleiner, Yehuda; Rajani, Balvant; Krys, Dennis

This paper describes a visual simultaneous localization and mapping (VSLAM) method for a pipe inspection robot that can serve as a carrier for nondestructive testing (NDT) sensors inside in-service water mains. The proposed method features a multi-sensor fusion system that simultaneously executes two important functions of the robot: construct a global image of the internal surface of the pipe and provide the location of the NDT sensors. The fusion involves a synergy between image mosaicing and dead reckoning. More precisely, the sequential images of a digital camera are stitched together with the help of an inertial navigation system (INS). In the proposed model, the processing load of image mosaicing is reduced significantly, and at the same time the accumulation of error of the INS is prevented. The functionality of the model is verified using a simulator that mimics the motion of a pipe inspection robot inside a water main.

No abstract

No abstract

/npsi/ctrl?lang=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?lang=fr Access and use of this website and the material on it are subject to the Terms and Conditions set forth at http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. For the publisher's version, please access the DOI link below./ Pour consulter la version de l'éditeur, utilisez le lien DOI ci-dessous.http://dx.doi.org/10. 1016/j.autcon.2011.06.002 Automation in Construction, 21, pp. 172-183, 2012-01-01 Improved laser scan for pitting corrosion measurement by using super resolution technique Liu, Z.; Krys, D.Improved laser scan for pitting corrosion measurement by using super resolution technique Liu, Z.; Krys, D. NRCC-54594A version of this document is published in : Automation in Construction, 21, pp. 172-183, January-01-12, DOI:10.1016/j.autcon.2011.06.002The material in this document is covered by the provisions of the Copyright Act, by Canadian laws, policies, regulations and international agreements. Such provisions serve to identify the information source and, in specific instances, to prohibit reproduction of materials without written permission. AbstractTo characterize the pitting corrosion of metallic pipe, high-resolution laser scan is indispensable. In many cases, only low-resolution scan can be obtained due to the limitations of the scanning equipment or time constraint. Although interpolation method can be applied to enlarge the low-resolution image, the enlarged laser scan loses the details of surface topography, which are important to calculate the parameters of pitting corrosion. In this paper, a singe-frame super resolution method is proposed to infer a high-resolution laser scan from the low-resolution input. The relation between the lowresolution input and high-resolution result is modeled with a Markov random field (MRF) with the aid of a training set built in advance. A belief propagation algorithm is implemented to infer the supre-resolved result. The experiments demonstrate a good performance of the proposed method in comparison with the traditional interpolation methods.