Track Geometry

Track geometry research encompasses the analysis, modeling, and utilization of track information for various applications, primarily focusing on improving accuracy, efficiency, and safety. Current research emphasizes robust methods for track mapping and localization, often employing techniques like neural networks (e.g., convolutional and recurrent architectures), particle filters, and sensor fusion strategies integrating data from LiDAR, cameras, and IMUs. These advancements are crucial for autonomous navigation in diverse contexts, from railway systems and autonomous vehicles to music composition and marine surveillance, enhancing safety, automation, and performance.