The article presents a method for 3D point cloud segmentation. The point cloud comes from a FARO LS scanner – the device creates a dense point cloud, where 3D points are organized in the 2D table. The input data set consists of millions of 3D points – it makes widely known RANSAC algorithms unusable. We add some modifi cations to use RANSAC for such big data sets.

The research was aimed at analysing the factors that affect the accuracy of merging point clouds when scanning over longer distances. Research takes into account the limited possibilities of target placement occurring while scanning opposite benches of quarries or open-pit mines, embankments from opposite banks of rivers etc. In all these cases, there is an obstacle/void between the scanner and measured object that prevents the optimal location of targets and enlarging scanning distances. The accuracy factors for cloud merging are: the placement of targets relative to the scanner and measured object, the target type and instrument range. Tests demonstrated that for scanning of objects with lower accuracy requirements, over long distances, it is optimal to choose flat targets for registration. For objects with higher accuracy requirements, scanned from shorter distances, it is worth selecting spherical targets. Targets and scanned object should be on the same side of the void.

Terrestrial laser scanner (TLS) is a new class of survey instruments to capture spatial data developed rapidly. A perfect facility in the oil industry does not exist. As facilities age, oil and gas companies often need to revamp their plants to make sure the facilities still meet their specifications. Due to the complexity of an oil plant site, there are difficulties in revamping, having all dimensions and geometric properties, getting through narrow spaces between pipes and having the description label of each object within a facility site. So it is needed to develop an accurate observations technique to overcome these difficulties. TLS could be an unconventional solution as it accurately measures the coordinates identifying the position of each object within the oil plant and provide highly detailed 3D models. This paper investigates creating 3D model for Ras Gharib oil plant in Egypt and determining the geometric properties of oil plant equipment (tank, vessels, pipes . . . etc.) using TLS observations and modeling by CADWORX program. The modeling involves an analysis of several scans of the oil plant. All the processes to convert the observed points cloud into a 3D model are described. The geometric properties for tanks, vessels and pipes (radius, center coordinates, height and consequently oil volume) are also calculated and presented. The results provide a significant improvement in observing and modeling of an oil plant and prove that the TLS is the most effective choice for generating a representative 3D model required for oil plant revamping.

Based on the theory of computer vision, a new method for extracting ore from underground mines is proposed. This is based on a combination of RGB images collected by a color industrial camera and a point cloud generated by a 3D ToF camera. Firstly, the mean-shift algorithm combined with the embedded confidence edge detection algorithm is used to segment the RGB ore image into different regions. Secondly, the effective ore regions are classified into large pieces of ore and ore piles consisting of a number of small pieces of ore. The method applied in the classification process is to embed the confidence into the edge detection algorithm which calculates edge distribution around ore regions. Finally, the RGB camera and the 3D ToF camera are calibrated and the camera matrix transformation of the two cameras is obtained. Point cloud fragments are then extracted according to the cross-calibration result. The geometric properties of the ore point cloud are then analysed in the subsequent procedure.

Transmission lines’ live working is one of an effective means to ensure the reliable operation of transmission lines. In order to solve the unsafe problems existing in the implementation of traditional live working, the paper uses ground-based lidar to collect point cloud data. A tile based on the pyramid data structure is proposed to complete the storage and calling of point cloud data. The improved bidirectional filtering algorithm is used to distinguish surface features quickly and obtain a 3D model of the site. Considering the characteristics of live working, the speed of data reading and querying, the nearest point search algorithm based on octree is used to acquire a real- time calculation of the safe distance of each point in the planned path, and the safety of the operation mode is obtained by comparing with the value specified in the regulation, and assist in making decisions of the operation plan. In the paper, the simulation of the actual working condition is carried out by taking the “the electric lifting device ascending” as an example. The experimental results show that the established three-dimensional model can meet the whole process control of the operation, and has achieved practical effect.

The number of scanner stations used to acquire point cloud data is limited, resulting in poor data registration. As a result, a cloud point block registration approach was proposed that took into account the distance between the point and the surface. When registering point cloud data, the invariant angle, length, and area of the two groups of point cloud data were affine transformed, and then the block registration parameters of point cloud data were determined. A finite hybrid model of point cloud data was created based on the coplane four-point nonuniqueness during the affine translation. On this basis, the point cloud data block registration algorithm was designed. Experimental results prove that the proposed method has great advantages in texture alignment, registration accuracy and registration time, so it is able to effectively improve the registration effect of point cloud data. The point cloud data block registration algorithm was built on this foundation. Experiments show that the suggested method has significant improvements in texture alignment, registration accuracy, and registration time, indicating that it can significantly improve point cloud data registration.

3D scanning measurements are gaining popularity every year. Quick inspections on already captured point clouds are easy to prepare with the use of modern software and machine learning. To achieve repeatability and accuracy, some surface and measurement issues should be considered and resolved before the inspection. Large numbers of manufacturing scans are not intended for manual correction. This article is a case study of a small surface inspection of a turbine guide vane based on 3D scans. Small surface errors cannot be neglected as their incorrect inspection can result in serious faults in the final product. Contour recognition and deletion seem to be a rational method for making a scan inspection with the same level of accuracy as we have now for CMM machines. The main reason why a scan inspection can be difficult is that the CAD source model can be slightly different from the inspected part. Not all details are always included, and small chamfers and blends can be added during the production process, based on manufacturing standards and best practices. This problem does not occur during a CMM (coordinate measuring machine) inspection, but it may occur in a general 3D scanning inspection.

Using a lower-cost laser scanner for generating accuracy in 3D point-cloud has been a concern because of economic issues; therefore, this study aims to create a 3D point cloud of a target object using a low-cost 2D laser scanner, Hokuyo UTM 30LX. The experiment was carried out in November 2019 with 16 single scans from 8 different viewpoints to capture the surface information of a structure object with many intricate details. The device was attached to a rail, and it could move with stable velocity thanks to an adjustable speed motor. The corresponding 16 point-clouds were generated by using the R language. Then, they were combined one by one to make a completed 3D point cloud in the united coordinate system. The resulted point cloud consisted of 1.4 million points with high accuracy (RMSE = 1:5 cm) is suitable for visualizing and assessing the target object thanks to high dense point-cloud data. Both small details and characters on the object surface can be recognized directly from the point cloud. This result confirms the ability of generated the accuracy point cloud from the low-cost 2D laser scanner Hokuyo UTM 30LX for 3D visualizing or indirectly evaluating the current situation of the target object.

Monitoring the technical condition of hydrotechnical facilities is crucial for ensuring their safe usage. This process typically involves tracking environmental variables (e.g., concrete damming levels, temperatures, piezometer readings) as well as geometric and physical variables (deformation, cracking, filtration, pore pressure, etc.), whose long-term trends provide valuable information for facility managers. Research on the methods of analyzing geodetic monitoring data (manual and automatic) and sensor data is vital for assessing the technical condition and safety of facilities, particularly when utilizing new measurement technologies. Emerging technologies for obtaining data on the changes in the surface of objects employ laser scanning techniques (such as LiDAR, Light Detection, and Ranging) from various heights: terrestrial, unmanned aerial vehicles (UAVs, drones), and satellites using sensors that record geospatial and multispectral data. This article introduces an algorithm to determine geometric change trends using terrestrial laser scanning data for both concrete and earth surfaces. In the consecutive steps of the algorithm, normal vectors were utilized to analyze changes, calculate local surface deflection angles, and determine object alterations. These normal vectors were derived by fitting local planes to the point cloud using the least squares method. In most applications, surface strain and deformation analyses based on laser scanning point clouds primarily involve direct comparisons using the Cloud to Cloud (C2C) method, resulting in complex, difficult-to-interpret deformation maps. In contrast, preliminary trend analysis using local normal vectors allows for rapid threat detection. This approach significantly reduces calculations, with detailed point cloud interpretation commencing only after detecting a change on the object indicated by normal vectors in the form of an increasing deflection trend. Referred to as the cluster algorithm by the authors of this paper, this method can be applied to monitor both concrete and earth objects, with examples of analyses for different object types presented in the article.

The integration of geodetic and photogrammetric data has become a new tool that has expanded the existing measurement capabilities, as well as it found its application outside the geodetic sector. As a result, over the past decades, the process of topographic data acquisition has caused cartographic industry to move from classical surveying methods to passive and active detection methods. The introduction of remote sensing technology has not only improved the speed of data acquisition but has also provided elevation data for areas that are difficult to access and survey. The aim of the work is to analyse consistency of elevation data from the Georeference Database of Topographic Objects (Pol. Baza danych obiektów topograficznych – BDOT500) with data from airborne laser scanning (ALS) for selected 15 research areas located in the City of Kraków. The main findings reveal discrepancies between elevation data sources, potentially affecting the accuracy of various applications, such as flood risk assessment, urban planning, and environmental management. The research gap identified in the study might stem from the lack of comprehensive investigations into the consistency and accuracy of elevation data across different databases and technologies in urban areas. This gap highlights the need for a thorough examination of the reliability of various data sources and methods of urban planning, disaster management, and environmental analysis. The integration of diverse databases and technologies, like ALS and geodetic measurements, in various applications introduces potential discrepancies that can significantly impact decision-making and outcomes.