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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 104
Edited by: J. Pombo
Paper 88

Bayesian Reconstruction of 3D Railway Track Geometry by Particle Filter

A. Yoshimura1 and Y. Naganuma2

1School of Computer Science, Tokyo University of Technology, Japan
2Nihon Kikai Hosen k. k., Tokyo, Japan

Full Bibliographic Reference for this paper
A. Yoshimura, Y. Naganuma, "Bayesian Reconstruction of 3D Railway Track Geometry by Particle Filter", in J. Pombo, (Editor), "Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Stirlingshire, UK, Paper 88, 2014. doi:10.4203/ccp.104.88
Keywords: reconstruction of 3D railway track geometry, three point chord versine measurements, parametric representation of 3D curve, self-organizing state-space model, local curvatures, recursive nonlinear filtering, Bayesian approach, sequential Monte Carlo particle filter.

In this paper, a new method of reconstructing three dimensional (3D) railway track geometry is described. The recursive nonlinear filtering based on the Bayesian approach is applied and implemented by the sequential Monte Carlo particle filter. Instead of reconstructing 2D planar curve independently in the vertical or horizontal direction, a new method makes use of 3-point chord versines for level and alignment simultaneously. By taking into account a mutual geometric dependency between the vertical direction and horizontal one, more accurate reconstruction becomes possible. In the new method, not only 3D railway track geometry but also local curvatures along the track in the vertical and horizontal directions are obtained. Examples are given and the effectiveness of the method is discussed.

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