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Low Grade Sensor Data Based Annotation of Topological Railway Maps

High quality road and track maps are an important factor in vehicle localization, since it allows for non-linear localization algorithms, such as Particle Filter and Extended Kalman Filter, to be run with over-the-shelf mobile sensing products. The current paper studies and experiments with a low-cost methodology of annotating open-source topological maps, with large quantities of crowd sourced sensor measurements for visual representation. All in an effort to reduce the cost of generating a visual representation of track geometry. Since rail lines can be easily represented as 1D distances for important track features, such as stations, so does the complexity of representation become more manageable and a direct annotation on the 2D track map more understandable.