ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5/W2, 223-228, 2013
https://doi.org/10.5194/isprsannals-II-5-W2-223-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
16 Oct 2013
Rail Track Detection and Modelling in Mobile Laser Scanner Data
S. Oude Elberink1, K. Khoshelham1, M. Arastounia2, and D. Diaz Benito3 1Faculty of Geo-Information Science and Earth Observation, University of Twente, the Netherlands
2Department of Geomatics Engineering, University of Calgary, Canada
3Grumets research group, CREAF, Universitat Autònoma de Barcelona, Catalonia, Spain
Keywords: Automation, Railway, MLS, Point Cloud, Classification, Fitting, Markov Chain, Monte Carlo, Interpolation Abstract. We present a method for detecting and modelling rails in mobile laser scanner data. The detection is based on the properties of the rail tracks and contact wires such as relative height, linearity and relative position with respect to other objects. Points classified as rail track are used in a 3D modelling algorithm. The modelling is done by first fitting a parametric model of a rail piece to the points along each track, and estimating the position and orientation parameters of each piece model. For each position and orientation parameter a smooth low-order Fourier curve is interpolated. Using all interpolated parameters a mesh model of the rail is reconstructed. The method is explained using two areas from a dataset acquired by a LYNX mobile mapping system in a mountainous area. Residuals between railway laser points and 3D models are in the range of 2 cm. It is concluded that a curve fitting algorithm is essential to reliably and accurately model the rail tracks by using the knowledge that railways are following a continuous and smooth path.
Conference paper (PDF, 1863 KB)


Citation: Oude Elberink, S., Khoshelham, K., Arastounia, M., and Diaz Benito, D.: Rail Track Detection and Modelling in Mobile Laser Scanner Data, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5/W2, 223-228, https://doi.org/10.5194/isprsannals-II-5-W2-223-2013, 2013.

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