3D Laser Point Cloud Research Articles

Precise Positioning Method of Moving Laser Point Cloud in Shield Tunnel Based on Bolt Hole Extraction

Mobile laser scanning technology used for deformation detection of shield tunnel is usually two-dimensional, which is expanded into three-dimensional (3D) through mileage, resulting in low positioning accuracy. This study proposes a 3D laser point cloud positioning method that is divided into rings in the mileage direction and blocks in the ring direction to improve the positional accuracy for shield tunnels. First, the cylindrical tunnel wall is expanded into a plane and the bolt holes are extracted using the self-adaptive parameter adjustment cloth simulation filter (CSF) algorithm combined with a density-based spatial clustering of applications with noise (DBSCAN) algorithm. Second, the mean-shift algorithm is used to obtain the center point of the bolt hole, and a model is designed to recognize the center point of different splicing blocks. Finally, the center point is combined with the standard straight-line equation to fit the straight-line positioning seam, achieving an accurate ring and block segmentation of a shield tunnel as a 3D laser point cloud. The proposed method is compared with existing methods to verify its feasibility and high accuracy using the seams located by the measured tunnel point cloud data and in the measured point cloud. The average differences between the circumferential seams positioned using the proposed method and those in the point cloud at the left waist, vault, and right waist were 3, 4, and 5 mm, respectively, and the average difference between the longitudinal seams was 3.4 mm The proposed research method provides important technical and theoretical support for tunnel safety monitoring and detection.

Segmentation and Stratification Methods of Field Maize Terrestrial LiDAR Point Cloud

Three-dimensional (3D) laser point cloud technology is an important research method in the field of agricultural remote sensing research. The collection and processing technology of terrestrial light detection and ranging (LiDAR) point cloud of crops has greatly promoted the integration of agricultural informatization and intelligence. In a smart farmland based on 3D modern agriculture, the manager can efficiently and conveniently achieve the growth status of crops through the point cloud collection system and processing model integrated in the smart agricultural system. To this end, we took field maize as the research object in this study and processed four sets of field maize point clouds, named Maize-01, Maize-02, Maize-03, and Maize-04, respectively. In this research, we established a field individual maize segmentation model with the density-based clustering algorithm (DBSCAN) as the core, and four groups of field maize were used as research objects. Among them, the value of the overall accuracy (OA) index, which was used to evaluate the comprehensive performance of the model, were 0.98, 0.97, 0.95, and 0.94. Secondly, the multi-condition identification method was used to separate different maize organ point clouds from the individual maize point cloud. In addition, the organ stratification model of field maize was established. In this organ stratification study, we take Maize-04 as the research object and obtained the recognition accuracy rates of four maize organs: tassel, stalk, ear, and leaf at 96.55%, 100%, 100%, and 99.12%, respectively. We also finely segmented the leaf organ obtained from the above-mentioned maize organ stratification model into each leaf individual again. We verified the accuracy of the leaf segmentation method with the leaf length as the representative. In the linear analysis of predicted values of leaf length, R2 was 0.73, RMSE was 0.12 m, and MAE was 0.07 m. In this study, we examined the segmentation of individual crop fields and established 3D information interpretations for crops in the field as well as for crop organs. Results visualized the real scene of the field, which is conducive to analyzing the response mechanism of crop growth and development to various complex environmental factors.

RGB-D Camera for 3D Laser Point Cloud Hole Repair in Mine Access Shaft Roadway

With the rapid development of the geographic information service industry, point cloud data are widely used in various fields, such as architecture, planning, cultural relics protection, mining engineering, etc. Despite that there are many approaches to collecting point clouds, we are facing the problem of point cloud holes caused by the inability of a 3D laser scanner to collect data completely in the narrow space of the mine access shaft. Thus, this paper uses RGB-D cameras to collect data and reconstruct the hole in the point cloud. We used a 3D laser scanner and RGB-D depth camera to collect the 3D point cloud data of the access shaft roadway. The maximum error was 2.617 cm and the minimum error was 0.031 cm by measuring the distance between the feature points, which satisfied the visualization repair of the missing parts of the 3D laser scanner data collection. We used the FPTH + ICP algorithm, ISS + ICP algorithm, SVD + ICP algorithm, and 3D-NDT algorithm to perform registration and fusion of the processed 3D point cloud and the original point cloud and finally repaired the hole. The study results show that the ISS + ICP registration algorithm had the most matching points and the lowest RMSE value of 13.8524 mm. In addition, in the closed and narrow roadway, the RGB-D camera was light and easy to operate and the point data acquired by it had relatively high precision. The three-dimensional point cloud of the repaired access shaft roadway has a good fit and can meet the repair requirements.

Segmentation of conifer tree crowns from terrestrial laser scanning point clouds in mixed stands of Scots pine and Norway spruce

Terrestrial laser scanning of conifer tree crowns is challenged by occlusion problems causing sparse point clouds for many trees. Automatic segmentation of conifer tree crowns from sparse point clouds is a task that has only recently been addressed and not solved in a way that all trees can be segmented automatically without assignment errors. We developed a new segmentation algorithm that is based on region growing from seeds in voxelized 3D laser point clouds. In our data, field measured tree positions and diameters were available as input data to estimate crown cores as seeds for the region growing. In other applications, these seeds can be derived from the laser point cloud. Segmentation success was judged visually in the 3D voxel clouds for 1294 tree crowns of Norway spruce and Scots pine on 24 plots in six mixed species stands. Only about half of the tree crowns had only minor or no segmentation errors allowing to fit concentric crown models. Segmentation errors were most often caused by unsegmented neighbors at the edge of the sample plots. Wrong assignments of crown parts were also more frequent in dense groups of trees and for understory trees. For some trees, point clouds were too sparse to describe the crown. Segmentation success rates were considerably higher for dominant trees in the plot center. Despite the incomplete automatic segmentation of tree crowns, metrics describing crown size and crown shape could be derived for a large number of sample trees. A description of the irregular shape of tree crowns was not possible for most trees due to the sparse point clouds in the upper crown of most trees.

PointLAE: A Point Cloud Semantic Segmentation Neural Network via Multifeature Aggregation for Large-Scale Application

The fast semantic segmentation algorithm of 3D laser point clouds for large scenes is of great significance for mobile information measurement systems, but the point cloud data is complex and generates problems such as disorder, rotational invariance, sparsity, severe occlusion, and unstructured data. We address the above problems by proposing the random sampling feature aggregation module ATSE module, which solves the problem of effective aggregation of features at different scales, and a new semantic segmentation framework PointLAE, which effectively presegments point clouds and obtains good semantic segmentation results by neural network training based on the features aggregated by the above module. We validate the accuracy of the algorithm by training on Semantic3D, a public dataset of large outdoor scenes, with an accuracy of 90.3, while verifying the robustness of the algorithm on Mvf CNN datasets with different sparsity levels, with an accuracy of 86.2, and on Bjfumap data aggregated by our own mobile environmental information collection platform, with an accuracy of 77.4, demonstrating that the algorithm is good for mobile information complex scale data in mobile information collection with great recognition effect.

Influence of Sliding Structure on Water Flowing Fracture Zone and Surface Subsidence in the Antidip of Overlying Strata Area

Sliding structure refers to the process and result of rock deformation caused by the continuous downward sliding and cutting of the upper stratum under the influence of gravity. In the process of tectonic formation, the strata go through two times of sliding to the south and then sliding to the north after the over thrusting and overstretching, so that the strata of the overthrusting and underthrusting strata are opposite to the inclination of the overthrusting and underthrusting strata, forming the anticlinal tectonic zone. Nowadays, under the conventional geological mining conditions, the underwater mining technology and surface subsidence laws are quite mature, but the fracture zone and surface movement characteristics of the sliding structure and the antioverturning mining are still rarely studied. This paper analyzes the dam damage in the mining process and discusses the three-dimensional reconstruction of laser scanning and the acquisition method of settlement information. The three-dimensional laser scanning technology is compared with the traditional monitoring technology. The deformation characteristics of surface structures above 25091 working face are revealed. The 3D laser point cloud shows that the uneven sinking of the dam is obvious and is associated with the uneven damage of the dam mass. The actual observation at the site shows that the uneven settlement of the dam on the surface was noticeable during the 25091 working face retraction. The damage on the east side of the dam was more serious and was accompanied by the generation of cracks running east-west. The DEM model of part of the surface and dam above the 25091 working face was obtained by 3D laser scanning technology. The model was analyzed to obtain information on surface subsidence. The study reveals that the uneven subsidence condition of the dam is apparent, with the overall characteristics of large subsidence on the east side of the dam and small subsidence on the west side of the dam. The displacement of the maximum surface subsidence point reflects the special phenomenon of overburden fracture and surface movement under the influence of overburden reverse inclination.

Finite element simulation of mechanical properties of Rosa roxburghii under compression loading

AbstractIt is very necessary to understand the mechanical properties of Rosa roxburghii fruit for harvesting, packaging, processing, transportation, storage, and related equipment design and improvement. This research was to determine the mechanical properties of R. roxburghii fruits through compressive experiments and finite element simulations and to verify the accuracy of the finite element model. In order to achieve finite element analysis, an accurate three‐dimensional (3D) geometric model was reconstructed by applying 3D laser technology and point cloud reconstruction algorithm. The elastic modulus and Poisson's ratio of R. roxburghii fruits were determined to be 4.0 MPa and 0.35 through experiments. The finite element simulation results provided valuable numerical data and visualized stress distribution. The simulation results showed that R. roxburghii fruits were damaged under the loads of 156.2 N (vertical) or 148.3 N (horizontal), with a maximum deformation of 5.0 mm (vertical) or 6.93 mm (horizontal), respectively. In the vertical and horizontal compression positions, the maximum relative errors between the experimental results and the finite element model are 24.71 and 13.99%, respectively. The results of the compressive strain energy of the fruit showed that the maximum relative error between the experimentally measured strain energy and the strain energy calculated by FEM simulation was 9.20 and 2.09% in the vertical and horizontal directions, respectively.Practical ApplicationsAs there is no relevant literature on the mechanical analysis of Rosa roxburghii fruit, the finite element method has been successfully applied to analyze the mechanical properties of R. roxburghii fruit under compressive load. At the same time, the simulation results have also been verified by experimental data, and the finite element technology has certain application prospects in food processing engineering. This study proposes a method based on 3D scanning technology and point cloud reconstruction algorithm to reconstruct an accurate geometric model of R. roxburghii fruit for finite element simulation, and conducts an in‐depth analysis on the stress, strain, and strain energy of the fruit. The purpose of this article is to propose a reliable method to predict the mechanical properties of the fruits under compressive load. The finite element simulation results can not only provide the basic data needed for fruit packaging and protection but also can be used for the design of automated fruit picking manipulators and the improvement of fruit processing equipment.

A Feature Based Laser SLAM Using Rasterized Images of 3D Point Cloud

An accurate and computationally efficient SLAM algorithm is vital for autonomous vehicles. Most modern SLAM systems use feature detection approaches to limit computational requirements. Feature detection through a 3D point cloud can be a computationally challenging task. In this paper, we propose a feature-based SLAM algorithm using 2D image projections of the 3D laser point cloud. We use a camera parameters matrix to rasterize the 3D point cloud to an image. Then ORB feature detector is applied to these images. The proposed method gives repeatable and stable features in a variety of environments. Based on such features, we can estimate the 6dof pose of the robot. For loop detection, we employ a 2-step approach, i.e., nearest key-frame detection and loop candidate verification by matching features extracted from rasterized LIDAR images. We evaluate the proposed system with implementation on the KITTI dataset. Through experimental results, we show that the algorithm presented in this paper can substantially reduce the computational cost of feature detection from the point cloud and the whole SLAM system while giving accurate results.

A Novel Method for Density Analysis of Repaired Point Cloud with Holes Based on Image Data

Repairing point cloud holes has become an important problem in the research of 3D laser point cloud data, which ensures the integrity and improves the precision of point cloud data. However, for the point cloud data with non-characteristic holes, the boundary data of point cloud holes cannot be used for repairing. Therefore, this paper introduces photogrammetry technology and analyzes the density of the image point cloud data with the highest precision. The 3D laser point cloud data are first formed into hole data with sharp features. The image data are calculated into six density image point cloud data. Next, the barycenterization Bursa model is used to fine-register the two types of data and to delete the overlapping regions. Then, the cross-section is used to evaluate the precision of the combined point cloud data to get the optimal density. A three-dimensional model is constructed for this data and the original point cloud data, respectively and the surface area method and the deviation method are used to compare them. The experimental results show that the ratio of the areas is less than 0.5%, and the maximum standard deviation is 0.0036 m and the minimum is 0.0015 m.

Research on Estimation Method of Geometric Features of Structured Negative Obstacle Based on Single-Frame 3D Laser Point Cloud

A single VLP-16 LiDAR estimation method based on a single-frame 3D laser point cloud is proposed to address the problem of estimating negative obstacles’ geometrical features in structured environments. Firstly, a distance measurement method is developed to determine the estimation range of the negative obstacle, which can be used to verify the accuracy of distance estimation. Secondly, the 3D point cloud of a negative obstacle is transformed into a 2D elevation raster image, making the detection and estimation of negative obstacles more intuitive and accurate. Thirdly, we compare the effects of a StatisticalOutlierRemoval filter, RadiusOutlier removal, and Conditional removal on 3D point clouds, and the effects of a Gauss filter, Median filter, and Aver filter on 2D image denoising, and design a flowchart for point cloud and image noise reduction and denoising. Finally, a geometrical feature estimation method is proposed based on the elevation raster image. The negative obstacle image in the raster is used as an auxiliary line, and the number of pixels is derived from the OpenCV-based Progressive Probabilistic Hough Transform to estimate the geometrical features of the negative obstacle based on the raster size. The experimental results show that the algorithm has high accuracy in estimating the geometric characteristics of negative obstacles on structured roads and has a practical application value for LiDAR environment perception research.

Collaborative 3D real modeling by multi-view images photogrammetry and laser scanning: The case study of Tangwei Village, China

Cultural heritage has extremely high historical significance and architectural aesthetic value. However, at present, the protection of many cultural heritage is facing great challenges, and it is urgent to get a complete and authentic record for preserving the memory of cultural heritage and the civilization it carries. The 3D real model can simultaneously record and express the geometric appearance and texture image information in the same 3D scene, which is one of the most comprehensive document types for recording the physical appearance authentic information of cultural heritage. Based on mature photogrammetry, aerial triangulation and computer vision technologies, a technology workflow for collaborative modeling for cultural heritage by multi-source data is proposed in this paper. Acquiring UAV low-altitude multi-view images, ground multi-view images and 3D laser point clouds data to record the appearance information of cultural heritage, the workflow through a series of technical processing processes to connect and integrated these data and their analysis results to generate a complete, detailed and high-resolution 3D real model, which is beneficial for overcoming a series of problems caused by the lack of sufficient ground information in the traditional technology workflow.

Fragmentation calculation method for blast muck piles in open-pit copper mines based on three-dimensional laser point cloud data

The current calculation methods for the blast fragmentation of muck piles (BFMP) do not consider three-dimensional (3D) feature information, such as concavity and convexity. Thus, their calculation results are inaccurate. In this work, 3D laser scanning technology with high precision, high efficiency, and real-time digitization is used to calculate fragmentation of blast muck piles in open-pit copper mines. The voxel cloud connectivity segmentation (VCCS) algorithm and locally convex connected patches (LCCP) algorithm are used to calculate BFMP from 3D laser point cloud data (3D LPCD). Meanwhile, an improved VCCS algorithm based on discrete features is proposed to solve the segmentation problem of 3D LPCD of small blast muck piles, which cannot be effectively segmented and thus greatly influence the 3D LPCD of large blast muck piles. An improved LCCP algorithm based on plane fitting solves the incorrect segmentation of the surface of large fragments into small pieces of fragments. Dexing Copper Mine, the largest open-pit copper mine in China, is taken as the research object. Results show that the accuracy of the calculation results is about 80% when the fragment sizes are 0.1–0.5 m. Moreover, almost all BFMP can be correctly calculated when the fragment size exceeds 0.5 m.

Reality Capture of Buildings Using 3D Laser Scanners

The urgent need to improve performance in the construction industry has led to the adoption of many innovative technologies. 3D laser scanners are amongst the leading technologies being used to capture and process assets or construction project data for use in various applications. Due to its nascent nature, many questions are still unanswered about 3D laser scanning, which in turn contribute to the slow adaptation of the technology. Some of these include the role of 3D laser scanners in capturing and processing raw construction project data. How accurate are the 3D laser scanner or point cloud data? How does laser scanning fit with other wider emerging technologies such as building information modeling (BIM)? This study adopts a proof-of-concept approach, which in addition to answering the aforementioned questions, illustrates the application of the technology in practice. The study finds that the quality of the data, commonly referred to as point cloud data, is still a major issue as it depends on the distance between the target object and 3D laser scanner’s station. Additionally, the quality of the data is still very dependent on data file sizes and the computational power of the processing machine. Lastly, the connection between laser scanning and BIM approaches is still weak as what can be done with a point cloud data model in a BIM environment is still very limited. The aforementioned findings reinforce existing views on the use of 3D laser scanners in capturing and processing construction project data.

Method for determining cloth simulation filtering threshold value based on curvature value of fitting curve

Cloth Simulation Filtering (CSF) is an algorithm that is effectively applied to mobile 3D laser point cloud filtering. The classification threshold in cloth simulation is a key parameter for separa...

Research on NDT-based Positioning for Autonomous Driving

Autonomous driving technology is one of the currently popular technologies, while positioning is the basic problem of autonomous navigation of autonomous vehicles. GPS is widely used as a relatively mature solution in the outdoor open road environment. However, GPS signals will be greatly affected in a complex environment with obstruction and electromagnetic interference, even signal loss may occur if serious, which has a great impact on the accuracy, stability and reliability of positioning. For the time being, L4 and most L3 autonomous driving modules still provide registration and positioning based on the high-precision map constructed. Based on this, this paper elaborates on the reconstruction of the experimental scene environment, using the SLAM (simultaneous localization and mapping) method to construct a highprecision point cloud map. On the constructed prior map, the 3D laser point cloud NDT matching method is used for real-time positioning, which is tested and verified on the “JAC Electric Vehicle” platform. The experimental results show that this algorithm has high positioning accuracy and its real-time performance meets the requirements, which can replace GPS signals to complete the positioning of autonomous vehicles when there is no GPS signal or the GPS signal is weak, and provide positioning accuracy meeting the requirements.

A Comparative Study for Indoor Planar Surface Segmentation via 3D Laser Point Cloud Data

In recent years, point cloud data generated with RGB-D cameras, 3D lasers, and 3D LiDARs have been employed frequently in robotic applications. In indoor environments, RGB-D cameras, which have short-range and can only describe the vicinity of the robots, generally are opted due to their low cost. On the other hand, 3D lasers and LiDARs can capture long-range measurements and generally are used in outdoor applications. In this study, we deal with the segmentation of indoor planar surfaces such as wall, floor, and ceiling via point cloud data. The segmentation methods, which are situated in Point Cloud Library (PCL) were executed with 3D laser point cloud data. The experiments were conducted to evaluate the performance of these methods with the publicly available Fukuoka indoor laser dataset, which has point clouds with different noise levels. The test results were compared in terms of segmentation accuracy and the time elapsed for segmentation. Besides, the general characteristics of each method were discussed. In this way, we revealed the positive and negative aspects of these methods for researchers that plan to apply them to 3D laser point cloud data.

Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain.

Obstacle detection is one of the essential capabilities for autonomous robots operated on unstructured terrain. In this paper, a novel laser-based approach is proposed for obstacle detection by autonomous robots, in which the Sobel operator is deployed in the edge-detection process of 3D laser point clouds. The point clouds of unstructured terrain are filtered by VoxelGrid, and then processed by the Gaussian kernel function to obtain the edge features of obstacles. The Euclidean clustering algorithm is optimized by super-voxel in order to cluster the point clouds of each obstacle. The characteristics of the obstacles are recognized by the Levenberg–Marquardt back-propagation (LM-BP) neural network. The algorithm proposed in this paper is a post-processing algorithm based on the reconstructed point cloud. Experiments are conducted by using both the existing datasets and real unstructured terrain point cloud reconstructed by an all-terrain robot to demonstrate the feasibility and performance of the proposed approach.

Efficient FPGA Implementation of K-Nearest-Neighbor Search Algorithm for 3D LIDAR Localization and Mapping in Smart Vehicles

K-Nearest-Neighbor search (KNN) has been extensively used in the localization and mapping based on 3D laser point clouds in smart vehicles. Considering the real-time requirement of localization and stringent battery constraint in smart vehicles, it is a great challenge to develop highly energy-efficient KNN implementations. Unfortunately, previous KNN implementations either cannot efficiently build search data structures or cannot search efficiently in massive and unevenly distributed point clouds. To solve the issue, we propose a new framework to optimize the implementation of KNN on FPGAs. First, we propose a novel data structure with a spatial subdivision method, which can be built efficiently even for massive point clouds. Second, based on our data structure, we propose a KNN search algorithm which is able to search in unevenly distributed point clouds efficiently. We have implemented the new framework on both FPGA and GPU. Energy efficiency results show that our proposed method is on average 2.1 times and 6.2 times higher than the state-of-the-art implementations of KNN on FPGA and GPU platform, respectively.

Optimized 3D laser point cloud reconstruction by gradient descent technique

Purpose This study aims to develop an optimized 3D laser point reconstruction using Descent Gradient algorithm. Precise and accurate reconstruction of 3D laser point cloud of the complex environment/object is a key solution for many industries such as construction, gaming, automobiles, aerial navigation, architecture and automation. A 2D laser scanner along with a servo motor/pan tilt/inertial measurement unit is used for generating 3D point cloud (either environment/object or both) by acquiring the real-time data from sensors. However, while generating the 3D laser point cloud, various problems related to time synchronization problem between laser and servomotor and torque variation in servomotors arise, which causes misalignment in stacking the 2D laser scan for generating the 3D point cloud of the environment. Because of the misalignment in stacking, the 2D laser scan corresponding to the erroneous angular and position information by the servomotor and the 3D laser point cloud become distorted in terms of inconsistency for measuring the dimension of the objects. Design/methodology/approach This paper addresses a modified 3D laser system assembled from a 2D laser scanner coupled with a servomotor (dynamixel motor) for developing an efficient 3D laser point cloud with the implementation of an optimization technique: descent gradient filter (DGT). The proposed approach reduces the cost function (error) in the angular and position coordinates of the servo motor caused because of torque variation and time synchronization, which resulted in enhancing the accuracy in 3D point cloud mapping for the accurate measurement of the object’s dimensions. Findings Various real-world experiments are performed with the proposed DGT filter linked with laser scanner and servomotor and an improvement of 6.5 per cent in measuring the accurate dimension of object is obtained while comparing with conventional approaches for generating a 3D laser point cloud. Originality/value This proposed technique may be applicable for various industrial applications that are based on robotics arms (such as painting, welding and cutting) in the automobile industry, the optimized measurement of object, efficient mobile robot navigation, precise 3D reconstruction of environment/object in construction, architecture applications, airborne applications and aerial navigation.

植物表面特征对三维激光点云的影响

植物表面特征对三维激光点云的影响