The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

The proper assessment of railroad condition requires the consideration of a number of factors. Some factors, such as the condition of the ties, can be measured by inspecting features visible from the surface of the railway. Other factors, such as the condition of the ballast, require subsurface measurements. Extensive human resources are currently applied to the problem of evaluating railroad health. Here we present the results of a study in automatic railroad classification that combines surface and subsurface measurements to characterize the railroad condition. To obtain surface measurements, we generate a 3-D profile of the railroad surface with a vision system that employs a laser light stripe. Subsurface measurements were made using ground penetrating radar (GPR). Principal component analysis was used to reduce the dimension of the raw data. Classifiers were trained on the resulting data using both memory based and Bayesian methods. The results are presented.

Keywords

railroad classification, pattern recognition, principal component analysis, ground penetrating radar

Notes

Sponsor: Federal Railroad Administration

Grant ID: DTFRDV-99-G-60015

Number of pages: 6

Text Reference

George A. Kantor, Herman Herman, Sanjiv Singh, John G. Tabacchi, and William Kaufman, "Automatic railway classification using surface and subsurface measurements," Proceeding of the 3rd International Conference on Field and Service Robotics (FSR 2001), 2001, pp. 43-48.

BibTeX Reference

@inproceedings{Kantor_2001_3867,
   author = "George A Kantor and Herman Herman and Sanjiv Singh and John G Tabacchi and William Kaufman",
   editor = "Aarne Halme, Raja Chatila, and Erwin Prassler",
   title = "Automatic railway classification using surface and subsurface measurements",
   booktitle = "Proceeding of the 3rd International Conference on Field and Service Robotics (FSR 2001)",
   pages = "43-48",
   publisher = "Yleisjalljennos",
   address = "Helsinki, Finland",
   year = "2001",
}

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