Przetwarzanie danych LIDAR dla potrzeb inwentaryzacji infrastruktury kolejowej

Purpose. To provide theoretical foundations and develop mathematical models for the efficient transformation of coordinate systems for point clouds in geophysical research; the scientific analysis is aimed at developing algorithms and establishing necessary dependencies for the reliable integration of data obtained at different time points into a unified coordinate system, opening up prospects for further study and analysis of processes in geophysical research. The methods.The calculation is carried out using the following steps. Determination of known coordinates of four points (x1', y1', z1'; x2', y2', z2'; x3', y3', z3'; x4', y4', z4') in a hypothetical coordinate system (X', Y', Z') and the coordinates of the same points (x1, y1, z1; x2, y2, z2; x3, y3, z3; x4, y4, z4) in the coordinate system (X, Y, Z) to which the point clouds need to be transformed. Determination of constants a1, a2, a3, d, b1, b2, b3, e, c1, c2, c3, f through a system of equations. After determining the constants, the coordinates of points (x', y', z') in the hypothetical coordinate system (X', Y', Z') are calculated using equations where each equation expresses the coordinates of points (x', y', z') in terms of coordinates of points (x, y, z) in the coordinate system (X, Y, Z) and the determined constants. After performing the calculations, point clouds can be merged into a single coordinate system using the computed coordinates (x', y', z'). This methodology allows for the successful transformation of coordinate systems for point clouds in geophysical research. Findings. Analytical regularities have been established based on known coordinates of four points in both coordinate systems, allowing for the efficient transformation of a point cloud from one coordinate system to another. The originality. For the first time, precise analytical dependencies have been established that enable the efficient transformation of point clouds from one coordinate system to another using known coordinates of four points in both systems. Practical implementation. The obtained dependencies enable the efficient transformation of point clouds from one coordinate system to another using known coordinates of four points in both systems.

In the paper the authors decided to present the capabilities of the application of the technology of mobile and airborne laser scanning both in surveying and modeling of railway infrastructure. First of all, they presented the possibility of using the laser scanning technology and in particular mobile laser scanning in terms of data collection and analysis for the tasks associated with the elaboration and diagnostics of railway infrastructure. For the mobile laser scanning the object of the study was the railway station in Slomniki, on a section of the railway line No. 8, and length of about 550 meters. The cloud of points together with photographic documentation was obtained using Riegl VMX - 450 system. Data from the airborne laser scanning included two different fragments of the railway line. The first one is located in Strzalkowo in the Wielkopolskie Voivodship (railway tracks along with the station building and the second one in Bochnia in the Malopolskie Voivodship. The section in Strzalkowo was about 470m and in Bochnia 302m. For example, the density of point cloud in Strzalkowo was 10 points/m2, while in Bochnia 17 points/m2.

Comparing Mobile and Aerial Laser Scanner point cloud data sets for automating the detection and delimitation procedure of safety-critical near-road slopes

Mangrove forests have unquestionably high environmental and ecological value. Mangrove trees are believed to have habitat zonation that is controlled mainly by the relative sea level. However, earlier discussions of mangrove habitats have remained limited in terms of their quality and quantity because of a lack of high-resolution spatial information of microtopography and trees. To clarify mangrove habitability over a wide forest area, we compounded mobile laser scanning (MLS) and aerial laser scanning (ALS) LiDAR dataset of the Miyara River mangrove on Ishigaki Island, Okinawa, Japan. The MLS provided sub-canopy data, while the unmanned aerial vehicle ALS data mainly provided a point cloud of the canopy. We corrected point clouds and combined these data. The results indicated that ALS is unable to reconstruct the microtopography of the dense mangrove area well. Moreover, tree species were not identifiable from the ALS data. However, by applying MLS to the mangrove forest, we obtained high-resolution microtopography and tree information inside the forest, although the measurement area was limited to comparison with ALS. By combining ALS and MLS point clouds, 3D point clouds of the forest were well reconstructed. From these point clouds, a high-resolution digital elevation model was created. Subsequently, we segmented trees individually from composite MLS point clouds and identified each tree species. Consequently, the spatial distribution of thousands of mangrove trees was reconstructed at the Miyara River mouth. The spatial distribution of mangrove tree species together with earlier aerial photographs suggests that mangrove species have been segregated in accordance with changes in their elevation and environment over 40 years. Our findings suggest that the distribution of the species changed sensitively along with dynamic variation of the microtopography.

Fast automatic three-dimensional road model reconstruction based on mobile laser scanning system

Sunlight conditions can reduce drivers’ visibility, which is a safety concern on road networks. This research introduces a method to study sun glare incidence in road environments. Sun glare areas during daylight hours are automatically detected from mobile laser scanning (MLS) and aerial laser scanning (ALS) point clouds. The method comprises the following steps. First, the Sun’s position (solar altitude and azimuth) referring to a location is calculated. Second, the incidence of sun glare with the user’s angle of vision is analyzed based on human vision. Third, sun ray intersections with near obstacles (vegetation, building, etc.) are calculated utilizing MLS point clouds. Finally, intersections with distant obstacles (mountains) are calculated utilizing ALS point clouds. MLS and ALS data are processed in order to combine both data types, remove outliers, and optimize computational time for intersection searches (point density reduction and Delaunay triangulation). The method was tested on two real case studies, covering roads with different bearings, slopes, and surroundings. The combination of MLS and ALS data, together with the solar geometry, identify areas of risk for the visibility of drivers. Consequently, the proposed method can be utilized to reduce sun glare, implementing warnings in navigation systems.

Nonrigid registration presents a significant challenge in the domain of point cloud processing. The general objective is to model complex nonrigid deformations between two or more overlapping point clouds. Applications are diverse and span multiple research fields, including registration of topographic data, scene flow estimation, and dynamic shape reconstruction. To provide context, the first part of the paper gives a general introduction to the topic of point cloud registration, including a categorization of existing methods. Then, a general mathematical formulation for the point cloud registration problem is introduced, which is then extended to address also nonrigid registration methods. A detailed discussion and categorization of existing approaches to nonrigid registration follows. In the second part of the paper, we propose a new method that uses piecewise tricubic polynomials for modeling nonrigid deformations. Our method offers several advantages over existing methods. These advantages include easy control of flexibility through a small number of intuitive tuning parameters, a closed-form optimization solution, and an efficient transformation of huge point clouds. We demonstrate our method through multiple examples that cover a broad range of applications, with a focus on remote sensing applications—namely, the registration of airborne laser scanning (ALS), mobile laser scanning (MLS), and terrestrial laser scanning (TLS) point clouds. The implementation of our algorithms is open source and can be found our public repository.

Abstract. The objective of the study was to benchmark the geometric accuracy of mobile laser scanning (MLS) systems using a permanent test field under good coverage of GNSS. Mobile laser scanning, also called mobile terrestrial laser scanning, is currently a rapidly developing area in laser scanning where laser scanners, GNSS and IMU are mounted onboard a moving vehicle. MLS can be considered to fill the gap between airborne and terrestrial laser scanning. Data provided by MLS systems can be characterized with the following technical parameters: a) point density in the range of 100-1000 points per m2 at 10 m distance, b) distance measurement accuracy of 2-5 cm, and c) operational scanning range from 1 to 100 m. Several commercial, including e.g. Riegl, Optech and others, and some research mobile laser scanning systems surveyed the test field using predefined driving speed and directions. The acquired georeferenced point clouds were delivered for analyzing. The geometric accuracy of the point clouds was determined using the reference targets that could be identified and measured from the point cloud. Results show that in good GNSS conditions most systems can reach an accuracy of 2 cm both in plane and elevation. The accuracy of a low cost system, the price of which is less than tenth of the other systems, seems to be within a few centimetres at least in ground elevation determination. Inaccuracies in the relative orientation of the instruments lead to systematic errors and when several scanners are used, in multiple reproductions of the objects. Mobile laser scanning systems can collect high density point cloud data with high accuracy. A permanent test field suits well for verifying and comparing the performance of different mobile laser scanning systems. The accuracy of the relative orientation between the mapping instruments needs more attention. For example, if the object is seen double in the point cloud due to imperfect boresight calibration between two scanners, this will make especially the automatic modelling of the object much more challenging.

Recently, point cloud technology has been applied in the ship engineering field. However, the dense point cloud acquired by terrestrial laser scanning (TLS) technology in ship engineering applications brings an obstacle to some powerful and advanced point-based deep learning point cloud processing methods. This paper presents a deep learning pre-procession module to ensure the feasibility of processing dense point clouds on commonly available computer devices. The pre-procession module is designed according to the traditional point cloud processing methods and the PointNet++ paradigm, and is evaluated on two ship structure datasets and two popular point cloud datasets. Experimental results illustrate that (i) the proposed module improves the performance of point-based deep learning semantic segmentation networks, and (ii) the proposed module empowers the existing point-based deep learning networks with the capability to process dense input point clouds. The proposed module may provide a useful semantic segmentation tool for realistic dense point clouds in various industrial applications.

The background noise of the photon counting laser point cloud data is large, and the distribution of point cloud density is uneven. In this paper, a point cloud denoising algorithm based on local distance weighted statistics is proposed to solve the problem that the noise point and non-noise are difficult to distinguish when the density of point cloud is low. By adding the weight function, the density difference between noise points and non-noise points is increased when the density of point cloud is low. This method can effectively remove the noise points and thus extract a continuous and complete effective point cloud. Besides, this paper compered the proposed method with the traditional point cloud denoising algorithm based on local distance statistics to verify the efficiency of the proposed algorithm. The results show that the algorithm in this paper has more advantages in dealing with the uneven density of laser point cloud.

Mobile laser scanning (MLS) has been successfully used for infrastructure monitoring apt to its fine accuracy and higher point density, which is favorable for object reconstruction. The massive data size, computational time, wider spatial distribution and feature extraction become a challenging task for 3D point data processing with MLS point cloud receives from terrestrial structures such as buildings, roads and railway tracks. In this paper, we propose a new approach to detect the structures in-line with railway track geometry such as railway crossings, turnouts and quantitatively estimate their dimensions and spatial location by iteratively applying a vertical slice to point cloud data for long distance laser measurement. The rectangular vertical slices were defined and their boundary coordinates were estimated based on a geometrical method. Estimated vertical slice boundaries were iteratively used to evaluate the point density of each vertical slice along with a cross-track direction of the railway line. Those point densities were further analyzed to detect the railway line track objects by their shape and spatial location along with the rail bed. Herein, the survey dataset is used as a dictionary to preidentify the spatial location of the object and then as an accurate estimation for the rail-track, by estimating the gauge corner (GC) from dense point cloud. The proposed method has shown a significant improvement in the rail-track extraction process, which becomes a challenge for existing remote sensing technologies. This adaptive object detection method can be used to identify the railway track structures prior to the railway track extraction, which allows in finding the GC position precisely. Further, it is based on the parallelism of the railway track, which is distinct from conventional railway track extraction methods. Therefore it does not require any inertial measurements along with the MLS survey and can be applied with less background information of the observed MLS point cloud. The proposed algorithm was tested for the MLS data set acquired during the pilot project collaborated with West Japan Railway Company. The results indicate 100% accuracy for railway structure detection and enhance the GC extraction for railway structure monitoring.

The point clouds acquired by a vehicle-borne mobile laser scanning (MLS) system have shown great potential for many applications such as intelligent transportation systems, road infrastructure inventories, and high-definition (HD) maps to support the advanced driver-assistance systems (ADAS) and autonomous vehicles (AVs). This paper presents a novel two-step approach to automated detection and reconstruction of three-dimensional (3D) highway curves from MLS point clouds. However, when dealing with noisy, unstructured, dense point clouds, we often face some challenges, most notably in handling of the outliers introduced during road marking detection and in recognition of curve types during 3D curve reconstruction. Our approach is formed by two main algorithms: a detector based on intensity variance and a robust model fitting estimator. The experimental results obtained using both a virtual scan dataset and a real MLS dataset demonstrated that our approach is very promising in handling of the outliers and reconstruction of 3D road curves. Specifically, a relative accuracy of 0.6% has been achieved in estimation of circle radii based on the virtual scan dataset. A comparative study also showed that our road marking detection approach is more effective and more stable than state-of-the-art approaches.

In recent years, 3D point cloud technology has played an increasingly important role in the field of casting parts detection. Due to the huge amount of high-precision point cloud data, simplification is often necessary before 3D reconstruction to improve computational efficiency. However, most of the existing point cloud simplification algorithms often blur edge details and produce holes in the final 3D reconstruction. To avoid these problems, a novel point cloud simplification method is proposed here. To accurately describe the geometric and texture features, a multi-featured fusion method was first developed to integrate the curvature, local projection distance, normal vector and local color differences. As this approach uses multiple types of geometric features, it accurately distinguishes sharp and smooth edges. Moreover, due to the use of local color difference, the texture features of the point cloud are recognizable. Then, to evaluate the density of the non-uniform point cloud, a globular neighborhood search strategy was used to estimate the density of the point cloud itself. Since the number of points in the spherical neighborhood reflected the sparsity of the point cloud, this method accurately represented the density of a point cloud with an uneven distribution. Finally, the feature fusion and density uniformity were integrated, allowing the use of these preserved points to retain more edge details of the point cloud in the final 3D reconstruction. This approach also avoided holes in the final 3D reconstruction. On the point cloud data of Anchor, Fantisk and Humana, the root mean square error of the proposed method reduces 40%, 32% and 19% than the best algorithm among the K-means clustering, Grid average, and Graph based method, respectively.

VPC-Net: Completion of 3D vehicles from MLS point clouds

Three-dimensional (3D) object recognition is crucial for intelligent autonomous agents such as autonomous vehicles and robots alike to operate effectively in unstructured environments. Most state-of-art approaches rely on relatively dense point clouds and performance drops significantly for sparse point clouds. Unsupervised domain adaption allows to minimise the discrepancy between dense and sparse point clouds with minimal unlabelled sparse point clouds, thereby saving additional sparse data collection, annotation and retraining costs. In this work, we propose a novel method for point cloud based object recognition with competitive performance with state-of-art methods on dense and sparse point clouds while being trained only with dense point clouds.