Track detection in 3D laser scanning data of railway infrastructure

Novel safety systems are needed to meet the growing demand of railway operation. In this paper we introduce general techniques for the detection of tracks and their components in 3D laser scanning data. These techniques make use of feature based methods, such as support vector machines, as well as model based methods, such as template matching. The focus of this work are robust and precise detectors for infrastructure elements, such as rails, tracks, closure rails, and frogs. These parts can be used for both, track maintenance and train-borne localization. The approach is evaluated experimentally on 3D laser scanning data and compared with a reference system. Furthermore, the approach is generic such that it can be used for data of any suitable laser scanning system.

[1]  Ettore Stella,et al.  A Real-Time Visual Inspection System for Railway Maintenance: Automatic Hexagonal-Headed Bolts Detection , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Carlo Tomasi,et al.  Depth Discontinuities by Pixel-to-Pixel Stereo , 1999, International Journal of Computer Vision.

[4]  Martin Lauer,et al.  A Train Localization Algorithm for Train Protection Systems of the Future , 2015, IEEE Transactions on Intelligent Transportation Systems.

[5]  Yusuf Sinan Akgul,et al.  Vision-based railroad track extraction using dynamic programming , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[6]  Sabine Van Huffel,et al.  Total least squares problem - computational aspects and analysis , 1991, Frontiers in applied mathematics.

[7]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.

[8]  Carsten Hasberg,et al.  Simultaneous Localization and Mapping for Path-Constrained Motion , 2012, IEEE Transactions on Intelligent Transportation Systems.

[9]  Ralph Ross.  Track and turnout detection in video-signals using probabilistic spline curves , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[10]  Christian Rahmig,et al.  Detecting Track Events with a Laser Scanner for using within a Modified Multi-Hypothesis Based Map-Matching Algorithm for Train Positioning , 2013 .

[11]  Carsten Hasberg,et al.  Probabilistic Rail Vehicle Localization With Eddy Current Sensors in Topological Maps , 2011, IEEE Transactions on Intelligent Transportation Systems.

[12]  Max Spindler,et al.  Rail detection using lidar sensors , 2016 .

[13]  Narendra Ahuja,et al.  Automated Visual Inspection of Railroad Tracks , 2013, IEEE Transactions on Intelligent Transportation Systems.

[14]  Max Spindler,et al.  An Analysis of Different Sensors for Turnout Detection for Train-borne Localization Systems , 2014 .

[15]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

[16]  A. Blug,et al.  Fast fiber coupled clearance profile scanner using real time 3D data processing with automatic rail detection , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[17]  Radu Bogdan Rusu,et al.  Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments , 2010, KI - Künstliche Intelligenz.

[18]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.