Laser Point Cloud Research Articles

Construction of Multi-resolution Spatial Data Organization for Ultralarge-scale 3D Laser Point Cloud

Construction of Multi-resolution Spatial Data Organization for Ultralarge-scale 3D Laser Point Cloud

Research on the Calculation and Analysis of Ski-Track Areas Based on Laser Point Clouds

To address the long-term statistical problem of ski-track area in the construction and operation of ski resorts, we propose a new ski-track point cloud boundary extraction method that improves the accuracy of boundary extraction and minimizes the offset of the area error. In this method, all point clouds are first projected onto the fitting plane using the random sample consensus (RANSAC) method. An improved point cloud boundary extraction algorithm is used to triangulate and extract the high-precision ski-track boundary. A discrete Green formula is then used to calculate and count the ski track’s exact area. It is demonstrated through five sets of test experiments that the error offset of the method proposed in this paper is smaller than that of other classical methods, which confirms its benefits and feasibility.

Wide-span timber trusses in the area of Bologna: a case study analysis and comparison

This paper summarizes a larger research project that used an innovative method to study several timber roofs structures in Bologna. The research was focused on developing a support tool for analyzing the geometry and the structural behavior of these structural systems, utilizing Terrestrial Laser Scanner (TLS) and point cloud geometric information via visual programming generative algorithms. In addition, the method has been developed to collect and trace data on various types of timber trusses and function as an information system. Specialized literature frequently oversimplifies the comprehension of these structural systems by basing its theories on structural analysis methods originated in the nineteenth century. This approach typically needs a thorough understanding of material properties and structure deformations, which cannot be obtained so easily.Innovative methods of research, as well as typological construction investigations, can be helpful to gain a thorough understanding of these objects. Such knowledge is essential for the conscious conservation of these amazing construction systems. This paper presents a comparative analysis of five types of timber trusses from the roofs of noticeable buildings from the 17th and 18th centuries, associating their typological and construction characteristics with the geometric and deformative information gathered in the previous research. The entire study provides a deeper understanding of these objects in the Italian context by highlighting some critical issues.

Digital characterization of the surface texture of chinese classical garden rockery based on point cloud visualization: small-rock mountain retreat

The Rockery is often a key element of a Classical Chinese Garden. It’s exquisite detailed physical characteristics a major contributor to artistic value, aesthetic appeal, and the carrier of historical and cultural heritage values. Poets and scholars have often described the beauty of these places in classical gardens in qualitative terms but lacked the quantitative tools to provide replicable metric descriptions. The highly complex forms and surfaces, irregularity, and fragility of garden rockeries has challenged authors to accurately describe the characteristics of these qualities using traditional methods and tools. This article presents a new method of digital characterization approach based on laser scanning and point cloud visualization, which can quantitatively detect and represent the pattern of rockery surface textures. It offers a replicable accurate quantitative descriptor of the Classical Chinese rockery. The Small-Rock Mountain Retreat, a nationally protected rockery garden in China, has been used as a case study. It contains original historic elements and more recently restored areas. Two characteristics of rockery surfaces, including the well-proportioned density and space, and the proper contrast between solid and void, were analyzed by examining four attributes: (1) surface complexity; (2) contour curvature; (3) shape variation; and (4) the interweaving of lightness and darkness. The findings demonstrate that, despite some similarities between the restored portion of the rockery and the historical remnants, there are variances in the richness of the details and the balanced distribution of shape change. The digital characterization approach introduced in this article offers a new perspective for recording and in turn safeguarding Chinese garden rockeries and other irregular cultural heritage objects.

Tree Species Classification Using Airborne LiDAR Data Based on Individual Tree Segmentation and Shape Fitting

Individual tree species classification is of strategic importance for forest monitoring, analysis, and management, which are critical for sustainable forestry development. In this regard, the paper proposes a method based on the profile of segmented individual tree laser scanning points to identify tree species. The proposed methodology mainly takes advantage of three-dimensional geometric features of a tree crown captured by a laser point cloud to identify tree species. Firstly, the Digital Terrain Model (DTM) and Digital Surface Model (DSM) are used for Crown Height Model (CHM) generation. Then, local maximum algorithms and improved rotating profile-based delineations are used to segment individual trees from the profile CHM point data. In the next step, parallel-line shape fitting is used to fit the tree crown shape. In particular, three basic geometric shapes, namely, triangle, rectangle, and arc are used to fit the tree crown shapes of different tree species. If the crown belongs to the same crown shape or shape combination, parameter classification is used, such as the ratio of crown width and crown height or the apex angle range of the triangles. The proposed method was tested by two real datasets which were acquired from two different sites located at Tiger and Leopard National Park in Northeast China. The experimental results indicate that the average tree classification accuracy is 90.9% and the optimal classification accuracy reached 95.9%, which meets the accuracy requirements for rapid forestry surveying.

Deformation Detection of Mining Tunnel Based on Automatic Target Recognition

Mining tunnels have irregular and diverse cross-sectional shapes. Structural deformation detection using mobile laser measurement has some problems, such as the inconvenient positioning of the deformation, difficulties in unifying the multiphase data, and difficulties in solving the section parameters. To address these problems, this paper proposes a mining tunnel deformation detection method based on automatic target recognition. Firstly, a mobile tunnel laser detection scheme combined with the target layout is designed. Secondly, a preview image of the tunnel lining is generated using the mobile laser point cloud data, and the index relationship between the image and point cloud is established. The target recognition accuracy of the You Only Look Once version 4 (YOLOv4) model is optimized by integrating the prediction confidence threshold, target spatial position, and target gray scale rule. Based on target recognition and positioning, the chord length and vault net height of the mining tunnel are calculated using gross error elimination and curve fitting. Finally, the engineering application of the model and algorithm is realized using ML.NET. The research method was verified using the field measurement data of the mining tunnel. The target recognition accuracy reached 100%, and the repeated deviations of the chord length and net height of the arch crown were 1.7 mm and 1.4 mm, respectively, which established the effectiveness and high accuracy of the research method.

The Baroque Renovation of the Medieval Franciscan Church of Jászberény

This study discusses the Franciscan church in Jászberény, originally built in the Middle Ages. The methods of Bauforschung/building archaeology were used in the attempt to create a theoretic recon-struction of the church's former appearance, involving laser scanning and point cloud data. The re-search focused mainly on the attic space, more specifically, on the question of the former vaults of the church.

A Method for Ground Filtering of Laser Point Cloud and Extraction of Tunnel Wall and Target Sphere

A Method for Ground Filtering of Laser Point Cloud and Extraction of Tunnel Wall and Target Sphere

Geometric Dimension Measurement Method for Bolster Spring Based on Three-Dimensional Laser Point Clouds

Geometric Dimension Measurement Method for Bolster Spring Based on Three-Dimensional Laser Point Clouds

Ancient Building Surveying and Mapping Modeling Protection System Based on Lagrangian Mathematical Equation

Abstract The primary data model of the current point cloud is still the organization of discrete points. Point clouds and patterns are independent of each other. Firstly, this paper establishes the data structure of multi-layer integrated ancient buildings based on the point cloud model. The purpose is to adapt to ancient buildings’ laser point cloud processing technology. Then this paper uses Lagrangian mathematics to model the point cloud pattern. Finally, the paper verifies that the cartographic model of ancient buildings established by the Lagrangian formula is smooth and accurate. The model can express detailed information about ancient buildings.

Automatic Registration Method of Vehicle‐Borne Laser Point Cloud Combining Ground Points and Rods

Automatic Registration Method of Vehicle‐Borne Laser Point Cloud Combining Ground Points and Rods

Direct generation of finite element mesh using 3D laser point cloud

An approach for the finite element (FE) mesh generation of tunnel structures considering the lack of detail, low automation, and intermediate link transformation in the FE mesh generation of tunnels based on the 3D laser point cloud is presented in this paper. To this end, we acquired the tunnel point cloud model using terrestrial laser scanning or mobile laser scanning, and uniform nodes from the raw point cloud using a clock simulation extraction algorithm we developed. In addition, we assign topology information to these nodes. Further, we generated an FE model of the tunnel structure suitable for numerical simulation analysis using Abaqus. We applied the developed method to three types of tunnels; the mesh satisfied the evaluation standards of practical engineering applications. We examined the impact of mesh size and used an engineering example to confirm the method's accuracy. Finally, we provide recommendations for future research considering the future trend of FE mesh generation based on the 3D laser point cloud and its improvements.

Fast and accurate registration of large scene vehicle-borne laser point clouds based on road marking information

Vehicle-borne Mobile Laser Scanning (MLS) point cloud is one of the key components of 3D spatial information, which provides important geographic data support for the development of smart cities and autonomous driving. However, urban high-rise buildings will obscure the Global Navigation Satellite System (GNSS) positioning signal of the vehicle-borne MLS system, causing the problem of the inconsistent location of revisited point clouds collected in the same area, and the existing methods are still limited by low accuracy and efficiency when registering large-scale point clouds. Aiming at the complexity and timeliness of vehicle-borne point cloud registration, this paper proposes an improved Iterative Closest Point (ICP) point cloud accurate registration method based on road markings. Firstly, the road feature image is generated based on the laser reflection intensity of the ground point cloud, and the Pix2Pix_L1 (P2P_L1) transformation model is introduced to realize the identification and classification of markings. Secondly, we take road classification markings as the registration primitives, a Random Sample Consensus (RANSAC) point cloud coarse registration method based on a Fast Point Feature Histogram (FPFH) descriptor is designed, and the Chi-square distance function is used to improve the stability of corresponding points. Then, according to the normal vector characteristics of marking point clouds, the angle between the normal vectors is constrained to extract edge points of markings and select effective corresponding closest point pairs, and the translation vector is preferentially calculated according to the offset characteristics of point cloud to optimize spatial transformation matrix. Finally, a linear interpolation method is developed to correct the pose of global point clouds. Experimental results show that our method achieves high registration accuracy and computational speed on multiple datasets, and has stronger performance than the most widely used geometric feature registration methods.

Improved LiDAR Localization Method for Mobile Robots Based on Multi-Sensing

In this paper, we propose a localization method applicable to 3D LiDAR by improving the LiDAR localization algorithm, such as AMCL (Adaptive Monte Carlo Localization). The method utilizes multiple sensing information, including 3D LiDAR, IMU and the odometer, and can be used without GNSS. Firstly, the wheel speed odometer and IMU data of the mobile robot are multi-source fused by EKF (Extended Kalman Filter), and the sensor data obtained after multi-source fusion are used as the motion model to participate in the positional prediction of the particle set in AMCL to obtain the initial positioning information of the mobile robot. Then, the position pose difference values output by AMCL at adjacent moments are substituted into the PL-ICP algorithm as the initial position pose transformation matrix, and the 3D laser point cloud is aligned with the nonlinear system using the PL-ICP algorithm. The three-dimensional laser odometer is obtained by LM (Levenberg--Marquard) iterative solution in the PL-ICP algorithm. Finally, the initial position pose output by AMCL is corrected by the three-dimensional laser odometer, and the AMCL particles are weighted and sampled to output the final positioning result of the mobile robot. Through simulation and practical experiments, it is verified that the improved AMCL algorithm has higher positioning accuracy and stability compared to the AMCL algorithm.

Application of 3D Design of Overhead Transmission Line Based on Laser Point Cloud Technology

The overhead transmission line is characterized by its high quantity, widespread, and complicated spot. Besides, it is easily influenced by external factors such as climate, geographical and social environment. In order to eliminate the influences above as much as possible, three-dimensional digital design is carried out in the design stage of the transmission line. Furthermore, the data collected by laser-point cloud technology, which can provide more accurate information, can be used to model geographic information rapidly and accurately, such as cross-over objects and digital elevation models. After that, based on the geographic information model imported into the 3D design platform, the ontology model of the overhead line can be completed. The overhead line model, like that, is able to be used for route optimization, check of the important cross, electric, and collision, as well as statistics on the cleaning of transmission line corridors. Compared with ordinary measurement data, laser point cloud technology can improve the design efficiency of overhead transmission line engineering, reduce design changes, and improve quality.

Ground point extraction using self-adaptive-grid and point to surface comparison

Extracting accurate ground points by using laser point clouds at the complex terrain of the mining area and the dense vegetation environment is a challenging problem. To address this problem, this paper proposes a combined filtering method based on the Self-Adaptive-Grid method (SAG) and Point to Surface Comparison (PSC). First, the adaptive grid is used to extract initial ground seed points, and then, based on the seed points, a Triangulated Irregular Network (TIN) is constructed, and the distance from the reference surface and TIN is computed. These steps are repeated, In each loop, the non-ground points that are significantly far away from the reference surface will be culled, until the ground point is close to the real terrain, where the initial grid size and the distance from the point to the reference surface are gradually reduced according to the gradient. The proposed algorithm overcomes the difficulty of extracting bare ground due to terrain fluctuations. The ground points are effectively separated from the non-ground points in the laser lidar scan data, and the extracted high-precision ground points can be used as important basic data in the terrain modeling of the mining area, surface subsidence monitoring, and research on the laws of surface subsidence, which has great value in popularization and application.

Research on a Point Cloud Registration Method of Mobile Laser Scanning and Terrestrial Laser Scanning

Mobile laser scanning can quickly and dynamically obtain a wide range of urban scene point clouds. However, due to factors such as occlusion and field of view limitation, it needs to be supplemented by terrestrial laser scanning. The acquisition methods and data quality of mobile point clouds and terrestrial point clouds are quite different, the target of urban scene point clouds is complex and diverse, and the corresponding feature is difficult to extract, so the point cloud fusion is difficult. To this end, a point cloud registration method of mobile and terrestrial scanning based on the target features of artificial ground objects is proposed. Firstly, the data features of mobile laser scanning point clouds and terrestrial laser scanning point clouds are analyzed, and the point clouds are diluted with equal density. Then, the artificial ground objects are extracted as the registration primitives to reduce the scene complexity, and the features of urban scenes and the features of point cloud eigenvalues and principal curvature attributes are analyzed. Combined with the octree voxel index, the multi-scale key point extraction method is constructed to extract the multi-scale key points of registration primitives. Finally, the key point constraint is used to improve the deficiencies of 4PCS (4-Points Congruent Sets) algorithm and ICP (Iterative Closest Point) algorithm to complete the registration of mobile and terrestrial point clouds in different road scenes. Experiments show that the point cloud registration accuracy can reach 2.6 cm, which provides a feasible method for high precision fusion of multi-platform laser point clouds.

Robotic 3-D Laser-Guided Approach for Efficient Cutting of Porcine Belly

Using autonomous meat cutting robots to cut porcine belly is a productive and more hygienic solution over traditional, labor-intensive meat cutting practices such as the transmission of the bacteria between the carcasses and manual laborers. Belly cutting is the first step of porcine meat segmentation. The robot needs to be precisely guided to cut open the porcine belly without damaging the guts inside, which facilitates subsequent processing of the guts. However, the body sizes of the pigs are different. As such, the 2-D and 3-D vision methods cannot be employed to identify the belly cutting path. In this article, a laser-guided efficient cutting path planning approach that combines the segmentation algorithm (SA) with an improved genetic algorithm (GA) is proposed. The robotic cutting system consists of an industrial robotic manipulator, customized tools, a linear laser sensor, and a PC. The salient features of the proposed scheme are as follows: 1) Spatial relationships of all the relevant units are established in Cartesian space. 2) Using the eigenvalue decomposition of the covariance matrix, the porcine belly cutting path is recognized from the 3-D laser point cloud of the carcass. 3) Aiming at efficiently optimizing the discrete path waypoints, an adaptive elite GA (AEGA) is derived by improving three operators of GA. 4) In order to alleviate the robotic cutting errors, the SA is designed to shorten the cutting path with no damage to the guts. 5) Under the limit of the peritoneal cavity, the efficient robotic cutting path is optimized by combining the SA with AEGA. Porcine belly cutting experiments are carried out in a porcine automation line under real-world conditions. Experimental results show that the proposed approach is accurate, efficient, and incurs lower costs in the long-term use of the robot.

A point cloud segmentation and material statistics algorithm for train carriage

The efficiency of train transportation in the port environment directly restricts the production efficiency of the entire port. In order to coordinate the productivity of the entire port and further improve the loading and unloading efficiency of the train automation system, this paper proposes a material segmentation and data statistics algorithm for the problem of material identification in the train compartment. The terrestrial laser scanning system was established under the experimental scenario of CHN ENERGY Tianjin Port. By fusing the laser point cloud data, and then performing pre-processing operations such as box filtering, down-sampling, and radius filtering, the pre-processed point cloud data is projected onto a two-dimensional plane as a two-dimensional image, and the canny operator is used to extract the contour of the carriage on the image. Further fitting to the plane of the point cloud of the carriage wall, and the point cloud of materials in the carriage is segmented. Through the method of slicing the compartment, completing the statistical analysis of the data in the compartment from the whole to the part. The database fields of the main carriage types are established to realize the information interaction between the database and the recognition algorithm. The final experimental results show that the error rate of the carriage length recognition is 5.89%, and the width error rate is 7.25%, which verifies that the algorithm has good recognition accuracy which fully meets engineering needs.

Indoor and outdoor multi-source 3D data fusion method for ancient buildings

Ancient buildings carry important information, such as ancient politics, economy, culture, customs. However, with the course of time, ancient buildings are often damaged to different degrees, so the restoration of ancient buildings is of great importance from the historical point of view. There are three commonly used non-contact measurement methods, including UAV-based oblique photogrammetry, terrestrial laser scanning, and close-range photogrammetry. These methods can provide integrated three-dimensional surveys of open spaces, indoor and outdoor surfaces for ancient buildings. Theoretically, the combined use of the three measurement methods can provide 3D (three-dimensional) data support for the protection and repair of ancient buildings. However, data from the three methods need to be fused urgently, because if the image data is not used, it will lead to a lack of real and intuitive texture information, and if only image matching point clouds are used, their accuracy will be lower than that of terrestrial laser scanning point clouds, and it will also lead to a lack of digital expression for components with high indoor historical value of ancient buildings. Therefore, in this paper, a data fusion method is proposed to achieve multi-source and multi-scale 3D data fusion of indoor and outdoor surfaces. It takes the terrestrial laser point cloud as the core, and based on fine component texture features and building outline features, respectively, the ground close-range image matching point cloud and UAV oblique image matching point cloud are registered with the terrestrial laser point cloud. This method unifies the data from three measurements in the point cloud and realizes the high-precision fusion of these three data. Based on the indoor and outdoor 3D full-element point cloud formed by the proposed method, it will constitute a visual point cloud model in producing plans, elevations, sections, orthophotos, and other elements for the study of ancient buildings.