MCMC Particle Filter with Overrelaxated Slice Sampling for Accurate Rail Inspection

This paper introduces a rail inspection system which detects rail flaws using computer vision algorithms. Unlike other methods designed for the same purpose, we propose a method that automatically fits a 3D rail model to the observations during regular services and normal traffic conditions. The proposed strategy is based on a novel application of the slice sampling technique with overrelaxation in the framework of MCMC (Markov Chain Monte Carlo) particle filters. This combination allows us to efficiently exploit the temporal coherence of observations and to obtain more accurate estimates than with other techniques such as importance sampling or Metropolis-Hastings. The results show that the system is able to efficient and robustly obtain measurements of the wear of the rails, while we show as well that it is possible to introduce the slice sampling technique into MCMC particle filters.