Abstract
This paper presents methods for 3D modeling of railway environments from airborne laser scanning (ALS) and mobile laser scanning (MLS). Conventionally, aerial data such as ALS and aerial images were utilized for 3D model reconstruction. However, 3D model reconstruction only from aerial-view datasets can not meet the requirement of advanced visualization (e.g., walk-through visualization). In this paper, objects in a railway environment such as the ground, railroads, buildings, high voltage powerlines, pylons and so on were reconstructed and visualized in real-life experiments in Kokemaki, Finland. Because of the complex terrain and scenes in railway environments, 3D modeling is challenging, especially for high resolution walk-through visualizations. However, MLS has flexible platforms and provides the possibility of acquiring data in a complex environment in high detail by combining with ALS data to produce complete 3D scene modeling. A procedure from point cloud classification to 3D reconstruction and 3D visualization is introduced, and new solutions are proposed for object extraction, 3D reconstruction, model simplification and final model 3D visualization. Image processing technology is used for the classification, 3D randomized Hough transformations (RHT) are used for the planar detection, and a quadtree approach is used for the ground model simplification. The results are visually analyzed by a comparison with an orthophoto at a 20 cm ground resolution.
Highlights
Over the last five years, an estimated US $300 billion worth of global investment has been expended to maintain and upgrade railway networks
The orthophoto was generated according to the terrain (DEM) from an airborne laser scanning (ALS) point cloud, and aerial images were acquired from the same platform as the ALS data
To make the algorithm more efficient, certain constraints are placed on the random initial points: (i) the distances between the points are less than 2 m; (ii) the three points are judged as being in a straight line or not, and only non-straight lines are used; and (iii) for each point, their normals are calculated; if the normals are in similar directions, the selection of those initial points was a success; otherwise, new initial points are randomly generated again
Summary
Over the last five years, an estimated US $300 billion worth of global investment has been expended to maintain and upgrade railway networks. Because of the complexity of the terrain in railway environments, the available data sources for recent 3D modeling of such scenes have been mainly from aerial views, including aerial images, airborne laser scanning (ALS), orthophotos, digital elevation models (DEMs), and ground plans. The modeling from those data sources usually produces rough results (see Figure 1). Due to flexible sensor platforms offering various data sources, e.g., ALS and MLS, and various available survey products like digital maps or ortho-photos, these data have provided the possibility for the complex 3D environment reconstruction. The remainder of the paper is organized as follows: Section 2 introduces the data sources for railway environment modeling; Section 3 presents the modeling methods; Section 4 includes the result and discussion; and Section 5 offers the conclusions
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