Abstract
An automatic method for rail inspection is introduced in this paper. The method detects rail flaws using computer vision algorithms. Unlike other methods designed for the same goal, we propose a method that automatically fits a 3D rail model to the observations. The proposed strategy is based on the novel combination of a simple but effective laser-camera calibration procedure with the application of an MCMC (Markov Chain Monte Carlo) framework. The proposed particle filter uses the efficient overrelation slice sampling method, which allows us to exploit the temporal coherence of observations and to obtain more accurate estimates than with other sampling techniques. The results show that the system is able to robustly obtain measurements of the wear of the rail. The two other contributions of the paper are the successfull introuction of the slice sampling technique into MCMC particle filters and the proposed online and flexible method for camera-laser calibration.
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Nieto, M., Cortés, A., Barandiaran, J., Otaegui, O., Etxabe, I. (2013). Single Camera Railways Track Profile Inspection Using an Slice Sampling-Based Particle Filter. In: Csurka, G., Kraus, M., Laramee, R.S., Richard, P., Braz, J. (eds) Computer Vision, Imaging and Computer Graphics. Theory and Application. Communications in Computer and Information Science, vol 359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38241-3_22
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DOI: https://doi.org/10.1007/978-3-642-38241-3_22
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