Rail Track Detection and Modelling in Mobile Laser Scanner Data

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Volumn 2, Issue 5W2, 2013, Pages 223-228

  Oude Elberink, S ^a   Khoshelham, K ^a   Arastounia, M ^b   Diaz Benito D ^c  

^a UNIVERSITY OF TWENTE   (Netherlands)

^b UNIVERSITY OF CALGARY   (Canada)

^c CREAF   (Spain)

Author keywords

Automation; Classification; Fitting; Interpolation; Markov Chain; MLS; Monte Carlo; Point Cloud; Railway

Indexed keywords

EID: 85006799672     PISSN: 21949042     EISSN: 21949050     Source Type: Conference Proceeding    
DOI: 10.5194/isprsannals-II-5-W2-223-2013     Document Type: Conference Paper

References (16)

1. Arastounia, M., 2012. Automatic classification of lidar point clouds in a railway environment. MSc thesis, University of Twente Faculty of Geo-Information and Earth Observation.
2. Beger, R., Gedrange, C., Hecht, R., Neubert, M., 2011. Data fusion of extremely high resolution aerial imagery and LiDAR data for automated railroad centre line reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing 66(2011), S40-S51.
3. Chan, T. And Lichti, D., 2011. 3D catenary curve fitting for geometric calibration. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-5/W12, 259-264.
4. Díaz Benito, D., 2012. Automatic 3D modelling of train rails in a lidar point cloud. MSc thesis, University of Twente Faculty of Geo-Information and Earth Observation (ITC).
5. Fischler, M.A. And Bolles, R.C., 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24(6): 381-395.
6. Gilks, W.R., Richardson, S., Spiegelhalter, D., 1995. Markov Chain Monte Carlo in Practice. Chapman and Hall/CRC, Boca Raton, Florida.
7. Hastings, W.K., 1970. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57(1), 97-109.
8. Leslar, M., Perry, G. And McNease, K., 2010. Using Mobile Lidar to Survey a Railway Line for Asset Inventory, ASPRS Annual Conference. ASPRS, San Diego, California, pp. on CD ROM.
9. Maas, H.-G. And Vosselman, G., 1999. Two Algorithms for Extracting Building Models from Raw Laser Altimetry Data. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 54(no. 2-3): 153-163.
10. Pal, M. And Foody, G.M., 2010. Feature selection for classification of hyperspectral data by SVM. IEEE Transactions on Geoscience and Remote Sensing, 48(5): 2297-2307.
11. Pal, M. And Foody, G.M., 2012. Evaluation of SVM, RVM and SMLR for accurate image classification with limited ground data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(5): 1344-1355.
12. Rabbani, T. And van den Heuvel, F., 2004. Methods for Fitting CSG Models to Point Clouds and Their Comparison, 7th IASTED International Conference on Computer Graphics and Imaging, Kauai, Hawaii, USA, pp. 279-284.
13. Rutzinger, M., Höfle, B., Oude Elberink, S. And Vosselman, G., 2011. Feasibility of Facade Footprint Extraction from Mobile Laser Scanning Data. PFG Photogrammetrie, Fernerkundung, Geoinformation, 2011(3): 97-107.
14. Tóvári, D. And Pfeifer, N., 2005. Segmentation based robust interpolation-a new approach to laser data filtering. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVI-3W19, 79-84.
15. Verma, V., Kumar, R. And Hsu, S., 2006. 3D Building Detection and Modeling from Aerial LIDAR Data, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06). IEEE Computer Society, Washington, DC, USA, pp. 2213-2220.
16. Werman, M. And Keren, D., 2001. A Bayesian method for fitting parametric and nonparametric models to noisy data. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 23(5): 528-534.