Based on IMBR algorithm, this paper proposes an algorithm for building contour extraction which is suitable for different scale digital line graphic (DLG). The new algorithm solves the tooth contour that may occur when IMBR algorithm is used to extract building contour. Firstly, a plane extraction algorithm based on region growth is used to extract the point cloud of the building roof. The algorithm introduces graded seed points and neighborhood-constrained growth criteria to avoid dividing the tree point cloud into building planes. Then, a Foot Point Correction algorithm is proposed to avoid the tooth contour in IMBR algorithm. The experimental results show that the Correctness and completeness of extracting building roof point cloud are above 97%. There is no tooth contour in the extracted building contour, which can meet the requirements of generating DLG with different scales.
Accurate and complete rail extraction from mobile laser scanning (MLS) data is currently a fundamental and challenging problem for its application on the railway. By using the track knowledge, a signed cylindrical neighborhood difference is defined as the rail descriptor and then proposed a new rail extraction algorithm from MLS data. It can extract accurate, continuous, and complete railhead, which is most critical for the rail geometric parameter and centerline, of the entire railway. Moreover, it can successfully extract the railhead of the main-line, including the curve section with different superelevation, and turnout. A 3-km long trunk railway, including main-line and turnout, straight line and curve line, located in the southwest of China is selected to test the performance of the proposed rail extraction algorithm. The experimental results show that the proposed algorithm can correctly extract the railhead of the whole railway, with an overall accuracy (F-measure) of 88.73%. Its accuracy is improved by 42.68% compared with the rail extraction algorithm based on spherical neighborhood difference.
With the rapid development of high-speed railway, there are many problems with the traditional railway slab assessment method. The traditional method is slow, and its precision is limited by the precision of specified tools for railway slab inspection. Scholars have developed a variety of inspection systems for railway slab geometry. Since those systems’ precision assessment relies on railway slab testing tools that are complex for operation, this paper proposes a novel method to assess the precision of an intelligent slab inspection system itself by using the spatial position deviation between the point cloud of a benchmark slab and the corresponding digital 3D model. The proposed method takes the RMSE of the deviation value of points in the key surfaces as the evaluation index. The key surfaces are the two shoulder surfaces and the rail-bearing surface of the rail-bearing platform, which can be extracted by the region growing algorithm associated with surface normals. Based on the real point cloud processed by an intelligent slab inspection system, the experimental results show that the system can align the slab point cloud to its corresponding 3D digital model. The deviation is distributed on the model uniformly, and its precision is 0.1 mm. In addition, this procedure is consistent with that of general slab inspection and can be used as a self-verification tool for daily precision evaluation of the system itself.
Digital elevation model (DEM) co-registration is one of the hottest research problems, and it is the critical technology for multi-temporal DEM analysis, which has wide potential application in many fields, such as geological hazards. Currently, the least-squares principle is used in most DEM co-registration methods, in which the matching parameters are obtained by iteration; the surface co-registration is then accomplished. To improve the iterative convergence rate, a Gauss-Newton method for DEM co-registration (G-N) is proposed in this paper. A gradient formula based on a gridded discrete surface is derived in theory, and then the difficulty of applying the Gauss-Newton method to DEM matching is solved. With the G-N algorithm, the surfaces approach each other along the maximal gradient direction, and therefore the iterative convergence and the performance efficiency of the new method can be enhanced greatly. According to experimental results based on the simulated datasets, the average convergence rates of rotation and translation parameters of the G-N algorithm are increased by 40 and 15% compared to those of the ICP algorithm, respectively. The performance efficiency of the G-N algorithm is 74.9% better.
As the 3D point-cloud data increase greatly with the development of data acquisition technology, it is an important prerequisite to establish an efficient index constructing and neighbor searching algorithm for point-cloud in data processing. In this paper, an efficient algorithm of index constructing and neighbor searching for 3D LiDAR data was proposed based on the combination of 3D grid, linear octree and Hash table. An experiment was conducted about the 3D LiDAR data and the result shows that the proposed method has higher efficiency of data index constructing and neighbor searching for 3D point-cloud acquired by mobile LiDAR.
Debris-flow is one of the major geological hazards in southwest China, which are a global threat and happen and results
in thousands deaths and injuries and billions of dollars in damages globally. Automatic multi-temporal DEM coregistration
for detecting terrain changes is an attractive but inherent very difficult research topic. Many methods have
been proposed in recent years, but all of them can only deal with DEM with limited percentage of terrain changes.
However, in landslide and debris-flow areas, the rate of terrain changes is very high. To solve such a problem, a new
method for detecting terrain changes using local invariant patches is proposed in this paper. According to the character of
the debris-flow activities, the peak and ridge are rarely affected. From where some invariant patches can be extracted
associated by the feature extraction method. After co-registration these invariant patches, a coarse matching can be
reached. Therefore, two DEM can be compared after applying this coarse matching. With an appropriate threshold, most
of terrain changes can be eliminated, and then a fine matching can be anticipated reasonable. The accuracy terrain
changes will be derived. The new method can estimate the terrain changes quantificationally and automatically, and
verifies by a real application. The experimental results illustrate the proposed method is of robust, accuracy, and timeefficiency.