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Analysis of cracking behaviors of five clayey materials using 3D point cloud data 认领 引用
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作者 Xin Wei Pengsen Wang +1 位作者 Yao Gao Ling Xu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第7期5731-5743,共13页
In recent years,drought-induced soil cracking has become increasingly prevalent,posing significant challenges to geotechnical engineering applications due to the associated hazards.Previous studies have investigated s... In recent years,drought-induced soil cracking has become increasingly prevalent,posing significant challenges to geotechnical engineering applications due to the associated hazards.Previous studies have investigated soil cracking behaviors and mechanisms via multi-scale methods.However,research on the spatial distribution characteristics of soil cracks and their depth evolution mechanisms is still rare.This paper analyzes the three-dimensional(3D)deformation of fiveclayey materials.In this study,3D surface elevation data of soil samples were acquired using a Gocator 3110 structured-light sensor.The reconstruction process involved point cloud acquisition and rasterization,Delaunay triangulation meshing,and calculation of crack depth.Different cracking behaviors are investigated,such as cracking in opening mode and shearing mode,coalescence and bifurcation of cracks,etc.The mineralogical analysis is carried out to reveal different cracking behaviors of clays.It is concluded that the crack depth of montmorillonite is larger than that of kaolin,reflectingits strong shrinkage characteristic,which makes the crack evolve deeper than the other materials.This technique enables multi-dimensional observation of soil crack propagation.The mineralogical analysis is carried out to reveal the different cracking behaviors of clays.It is concluded that microstructure and mineralogy are key factors influencingcracking behavior.Understanding soil cracking behaviors is significantfor engineering protection,geohazard prevention,and geological monitoring. 展开更多
关键词 Clay Free desiccation Laser scanning technique Three-dimensional(3D)point cloud data Cracking behavior
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Research on Airborne Point Cloud Data Registration Using Urban Buildings as an Example 认领 引用
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作者 Yajun Fan Yujun Shi +1 位作者 Chengjie Su Kai Wang 《Journal of World Architecture》 2025年第4期35-42,共8页
Airborne LiDAR(Light Detection and Ranging)is an evolving high-tech active remote sensing technology that has the capability to acquire large-area topographic data and can quickly generate DEM(Digital Elevation Model)... Airborne LiDAR(Light Detection and Ranging)is an evolving high-tech active remote sensing technology that has the capability to acquire large-area topographic data and can quickly generate DEM(Digital Elevation Model)products.Combined with image data,this technology can further enrich and extract spatial geographic information.However,practically,due to the limited operating range of airborne LiDAR and the large area of task,it would be necessary to perform registration and stitching process on point clouds of adjacent flight strips.By eliminating grow errors,the systematic errors in the data need to be effectively reduced.Thus,this paper conducts research on point cloud registration methods in urban building areas,aiming to improve the accuracy and processing efficiency of airborne LiDAR data.Meanwhile,an improved post-ICP(Iterative Closest Point)point cloud registration method was proposed in this study to determine the accurate registration and efficient stitching of point clouds,which capable to provide a potential technical support for applicants in related field. 展开更多
关键词 Airborne LiDAR Point cloud registration Point cloud data processing Systematic error
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Automated Rock Detection and Shape Analysis from Mars Rover Imagery and 3D Point Cloud Data 认领 引用 被引量：11
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作者 邸凯昌 岳宗玉 +1 位作者 刘召芹 王树良 《Journal of Earth Science》 SCIE EI CAS CSCD 2013年第1期125-135,共11页
A new object-oriented method has been developed for the extraction of Mars rocks from Mars rover data. It is based on a combination of Mars rover imagery and 3D point cloud data. First, Navcam or Pancam images taken b... A new object-oriented method has been developed for the extraction of Mars rocks from Mars rover data. It is based on a combination of Mars rover imagery and 3D point cloud data. First, Navcam or Pancam images taken by the Mars rovers are segmented into homogeneous objects with a mean-shift algorithm. Then, the objects in the segmented images are classified into small rock candidates, rock shadows, and large objects. Rock shadows and large objects are considered as the regions within which large rocks may exist. In these regions, large rock candidates are extracted through ground-plane fitting with the 3D point cloud data. Small and large rock candidates are combined and postprocessed to obtain the final rock extraction results. The shape properties of the rocks （angularity, circularity, width, height, and width-height ratio） have been calculated for subsequent ~eological studies. 展开更多
关键词 Mars rover rock extraction rover image 3D point cloud data.
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Methodology for Extraction of Tunnel Cross-Sections Using Dense Point Cloud Data 认领 引用 被引量：8
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作者 Yueqian SHEN Jinguo WANG +2 位作者 Jinhu WANG Wei DUAN Vagner G.FERREIRA 《Journal of Geodesy and Geoinformation Science》 CSCD 2021年第2期56-71,共16页
Tunnel deformation monitoring is a crucial task to evaluate tunnel stability during the metro operation period.Terrestrial Laser Scanning(TLS)can collect high density and high accuracy point cloud data in a few minute... Tunnel deformation monitoring is a crucial task to evaluate tunnel stability during the metro operation period.Terrestrial Laser Scanning(TLS)can collect high density and high accuracy point cloud data in a few minutes as an innovation technique,which provides promising applications in tunnel deformation monitoring.Here,an efficient method for extracting tunnel cross-sections and convergence analysis using dense TLS point cloud data is proposed.First,the tunnel orientation is determined using principal component analysis(PCA)in the Euclidean plane.Two control points are introduced to detect and remove the unsuitable points by using point cloud division and then the ground points are removed by defining an elevation value width of 0.5 m.Next,a z-score method is introduced to detect and remove the outlies.Because the tunnel cross-section’s standard shape is round,the circle fitting is implemented using the least-squares method.Afterward,the convergence analysis is made at the angles of 0°,30°and 150°.The proposed approach’s feasibility is tested on a TLS point cloud of a Nanjing subway tunnel acquired using a FARO X330 laser scanner.The results indicate that the proposed methodology achieves an overall accuracy of 1.34 mm,which is also in agreement with the measurements acquired by a total station instrument.The proposed methodology provides new insights and references for the applications of TLS in tunnel deformation monitoring,which can also be extended to other engineering applications. 展开更多
关键词 cross-section control point convergence analysis z-score method terrestrial laser scanning dense point cloud data
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PH-shape:an adaptive persistent homology-based approach for building outline extraction from ALS point cloud data 认领 引用 被引量：1
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作者 Gefei Kong Hongchao Fan 《Geo-Spatial Information Science》 SCIE EI CSCD 2024年第4期1107-1117,共11页
Building outline extraction from segmented point clouds is a critical step of building footprint generation.Existing methods for this task are often based on the convex hull and α-shape algorithm.There are also some ... Building outline extraction from segmented point clouds is a critical step of building footprint generation.Existing methods for this task are often based on the convex hull and α-shape algorithm.There are also some methods using grids and Delaunay triangulation.The common challenge of these methods is the determination of proper parameters.While deep learning-based methods have shown promise in reducing the impact and dependence on parameter selection,their reliance on datasets with ground truth information limits the generalization of these methods.In this study,a novel unsupervised approach,called PH-shape,is proposed to address the aforementioned challenge.The methods of Persistence Homology(PH)and Fourier descriptor are introduced into the task of building outline extraction.The PH from the theory of topological data analysis supports the automatic and adaptive determination of proper buffer radius,thus enabling the parameter-adaptive extraction of building outlines through buffering and“inverse”buffering.The quantitative and qualitative experiment results on two datasets with different point densities demonstrate the effectiveness of the proposed approach in the face of various building types,interior boundaries,and the density variation in the point cloud data of one building.The PH-supported parameter adaptivity helps the proposed approach overcome the challenge of parameter determination and data variations and achieve reliable extraction of building outlines. 展开更多
关键词 Building outline extraction point cloud data persistent homology boundary tracing
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Indoor Space Modeling and Parametric Component Construction Based on 3D Laser Point Cloud Data 认领 引用
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作者 Ruzhe Wang Xin Li Xin Meng 《Journal of World Architecture》 2023年第5期37-45,共9页
In order to enhance modeling efficiency and accuracy,we utilized 3D laser point cloud data for indoor space modeling.Point cloud data was obtained with a 3D laser scanner and optimized with Autodesk Recap and Revit so... In order to enhance modeling efficiency and accuracy,we utilized 3D laser point cloud data for indoor space modeling.Point cloud data was obtained with a 3D laser scanner and optimized with Autodesk Recap and Revit software to extract geometric information about the indoor environment.Furthermore,we proposed a method for constructing indoor elements based on parametric components.The research outcomes of this paper will offer new methods and tools for indoor space modeling and design.The approach of indoor space modeling based on 3D laser point cloud data and parametric component construction can enhance modeling efficiency and accuracy,providing architects,interior designers,and decorators with a better working platform and design reference. 展开更多
关键词 3D laser scanning technology Indoor space point cloud data Building information modeling(BIM)
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Construction Method of Building 3D Model Based on Oblique Image and Point Cloud Data 认领 引用
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作者 BAIYi 《外文科技期刊数据库（文摘版）工程技术》 2022年第3期107-111,共5页
Due to the inconsistent hue of the three-dimensional building model, the completeness and accuracy of model construction are low. Therefore, a method of building three-dimensional building model based on oblique image... Due to the inconsistent hue of the three-dimensional building model, the completeness and accuracy of model construction are low. Therefore, a method of building three-dimensional building model based on oblique images and point cloud data is proposed. The oblique photography technology is adopted to obtain the oblique images of buildings, and the relevant information is configured and preprocessed through uniform light and color. Three-dimensional laser scanner is adopted to acquire the building point cloud data, and the data are preprocessed by a simplified algorithm. The pre-processed oblique images and point cloud data are registered by using the closest point iteration algorithm (ICP). Based on the registration results, the computer-generated architecture (CGA) rules are applied to construct the three-dimensional building model. The experimental results show that compared with the existing methods, the proposed method improves the 3D model construction integrity by 11.57%, and the 3D model construction integrity and accuracy of the building sub-part of the proposed method are higher, which fully shows that the proposed method has better construction performance. 展开更多
关键词 oblique images point cloud data construction three-dimensional model construct
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TQU-GraspingObject:3D Common Objects Detection,Recognition,and Localization on Point Cloud for Hand Grasping in Sharing Environments 认领 引用
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作者 Thi-Loan Nguyen Huy-Nam Chu +2 位作者 The-Thanh Hua Trung-Nghia Phung Van-Hung Le 《Computers, Materials & Continua》 SCIE EI 2026年第5期1701-1722,共22页
To support the process of grasping objects on a tabletop for the blind or robotic arm,it is necessary to address fundamental computer vision tasks,such as detecting,recognizing,and locating objects in space,and determ... To support the process of grasping objects on a tabletop for the blind or robotic arm,it is necessary to address fundamental computer vision tasks,such as detecting,recognizing,and locating objects in space,and determining the position of the grasping information.These results can then be used to guide the visually impaired or to execute grasping tasks with a robotic arm.In this paper,we collected,annotated,and published the benchmark TQUGraspingObject dataset for testing,validation,and evaluation of deep learning(DL)models for detecting,recognizing,and localizing grasping objects in 2D and 3D space,especially 3D point cloud data.Our dataset is collected in a shared room,with common everyday objects placed on the tabletop in jumbled positions by Intel RealSense D435(IR-D435).This dataset includes more than 63k RGB-D pairs and related data such as normalized 3D object point cloud,3D object point cloud segmented,coordinate system normalizationmatrix,3D object point cloud normalized,and hand pose for grasping each object.At the same time,we also conducted experiments on fourDL networks with the best performance:SSD-MobileNetV3,ResNet50-Transformer,ResNet101-Transformer,and YOLOv12.The results present that YOLOv12 has the most suitable results in detecting and recognizing objects in images.All data,annotations,toolkit,source code,point cloud data,and results are publicly available on our project website:http://gffzz188fe103f8f1460as09pk5fx0ffpp609p.ffgz.tsg.suse.edu.cn/HuaTThanhIT2327Tqu/datasetv2. 展开更多
关键词 Grasping object of blind/Robot arm TQU-graspingobject benchmark dataset 3D point cloud data deep learning(DL) object detectionecognition intel realsense D435(IR-D435)
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Classification of rice seed variety using point cloud data combined with deep learning 认领 引用 被引量：8
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作者 Yan Qian Qianjin Xu +4 位作者 Yingying Yang Hu Lu Hua Li Xuebin Feng Wenqing Yin 《International Journal of Agricultural and Biological Engineering》 SCIE 2021年第5期206-212,共7页
Rice variety selection and quality inspection are key links in rice planting.Compared with two-dimensional images,three-dimensional information on rice seeds shows the appearance characteristics of rice seeds more com... Rice variety selection and quality inspection are key links in rice planting.Compared with two-dimensional images,three-dimensional information on rice seeds shows the appearance characteristics of rice seeds more comprehensively and accurately.This study proposed a rice variety classification method using three-dimensional point cloud data of the surface of rice seeds combined with a deep learning network to achieve the rapid and accurate identification of rice varieties.First,a point cloud collection platform was set up with a Raytrix light field camera as the core to collect three-dimensional point cloud data on the surface of rice seeds;then,the collected point cloud was filled,filtered and smoothed;after that,the point cloud segmentation is based on the RANSAC algorithm,and the point cloud downsampling is based on a combination of random sampling algorithm and voxel grid filtering algorithm.Finally,the processed point cloud was input to the improved PointNet network for feature extraction and species classification.The improved PointNet network added a cross-level feature connection structure,made full use of features at different levels,and better extracted the surface structure features of rice seeds.After testing,the improved PointNet model had an average classification accuracy of 89.4%for eight varieties of rice,which was 1.2%higher than that of the PointNet model.The method proposed in this study combined deep learning and point cloud data to achieve the efficient classification of rice varieties. 展开更多
关键词 rice seed variety classification point cloud data deep learning light field camera
Classified denoising method for laser point cloud data of stored grain bulk surface based on discrete wavelet threshold 认领 引用 被引量：1
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作者 Shao Qing Xu Tao +2 位作者 Yoshino Tatsuo Song Nan Zhu Hang 《International Journal of Agricultural and Biological Engineering》 SCIE 2016年第4期123-131,共9页
Surfaces of stored grain bulk are often reconstructed from organized point sets with noise by 3-D laser scanner in an online measuring system.As a result,denoising is an essential procedure in processing point cloud d... Surfaces of stored grain bulk are often reconstructed from organized point sets with noise by 3-D laser scanner in an online measuring system.As a result,denoising is an essential procedure in processing point cloud data for more accurate surface reconstruction and grain volume calculation.A classified denoising method was presented in this research for noise removal from point cloud data of the grain bulk surface.Based on the distribution characteristics of cloud point data,the noisy points were divided into three types:The first and second types of the noisy points were either sparse points or small point cloud data deviating and suspending from the main point cloud data,which could be deleted directly by a grid method;the third type of the noisy points was mixed with the main body of point cloud data,which were most difficult to distinguish.The point cloud data with those noisy points were projected into a horizontal plane.An image denoising method,discrete wavelet threshold(DWT)method,was applied to delete the third type of the noisy points.Three kinds of denoising methods including average filtering method,median filtering method and DWT method were applied respectively and compared for denoising the point cloud data.Experimental results show that the proposed method remains the most of the details and obtains the lowest average value of RMSE(Root Mean Square Error,0.219)as well as the lowest relative error of grain volume(0.086%)compared with the other two methods.Furthermore,the proposed denoising method could not only achieve the aim of removing noisy points,but also improve self-adaptive ability according to the characteristics of point cloud data of grain bulk surface.The results from this research also indicate that the proposed method is effective for denoising noisy points and provides more accurate data for calculating grain volume. 展开更多
关键词 point cloud data denoising grid method discrete wavelet threshold(DWT)method 3-D laser scanning stored grain
Detecting vertices of building roofs from ALS point cloud data 认领 引用
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作者 Gefei Kong Yi Zhao Hongchao Fan 《International Journal of Digital Earth》 SCIE EI 2023年第2期4811-4830,共20页
Roof vertex information is vital for 3D roof structures.Reconstructing 3D roof structures from point cloud data using traditional methods remains a challenge because their extracted roof vertices are affected by uncer... Roof vertex information is vital for 3D roof structures.Reconstructing 3D roof structures from point cloud data using traditional methods remains a challenge because their extracted roof vertices are affected by uncertainty and additional errors from roof plane segmentation and supplementary sub-steps for extracting primitives.In this study,instead of segmenting roof planes and then extracting primitives based on them,a flexible rule-based method is proposed to directly detect the vertices of building roofs from point cloud data without the requirement of training data.The point cloud data is first voxelized with a dominant direction-based rotation.Based on the different features of the interior roof points and vertices,rules for voxel filtering and structure line determination are defined to extract the roof vertices.The experimental results on a custom dataset in Trondheim,Norway demonstrate that the proposed method can effectively and accurately extract roof vertices from point cloud data.The comparative experimental results with an unfine-tuned deep learning-based method on custom and benchmark datasets with different point densities further show that the proposed method has good generalization and can adapt to changes of datasets. 展开更多
关键词 Roof vertex detection 3D roof structure voxelization rule-based point cloud data
Development of vehicle-recognition method on water surfaces using LiDAR data:SPD2(spherically stratified point projection with diameter and distance) 认领 引用
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作者 Eon-ho Lee Hyeon Jun Jeon +2 位作者 Jinwoo Choi Hyun-Taek Choi Sejin Lee 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2024年第6期95-104,共10页
Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface ... Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework. 展开更多
关键词 Object classification Clustering 3D point cloud data LiDAR(light detection and ranging) Surface vehicle
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Accuracy of common stem volume formulae using terrestrial photogrammetric point clouds:a case study with savanna trees in Benin 认领 引用
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作者 Hospice A.Akpo Gilbert Atindogbe +3 位作者 Maxwell C.Obiakara Arios B.Adjinanoukon Madai Gbedolo Noel H.Fonton 《Journal of Forestry Research》 SCIE CAS CSCD 2021年第6期2415-2422,共8页
Recent applications of digital photogrammetry in forestry have highlighted its utility as a viable mensuration technique.However,in tropical regions little research has been done on the accuracy of this approach for s... Recent applications of digital photogrammetry in forestry have highlighted its utility as a viable mensuration technique.However,in tropical regions little research has been done on the accuracy of this approach for stem volume calculation.In this study,the performance of Structure from Motion photogrammetry for estimating individual tree stem volume in relation to traditional approaches was evaluated.We selected 30 trees from five savanna species growing at the periphery of the W National Park in northern Benin and measured their circumferences at different heights using traditional tape and clinometer.Stem volumes of sample trees were estimated from the measured circumferences using nine volumetric formulae for solids of revolution,including cylinder,cone,paraboloid,neiloid and their respective fustrums.Each tree was photographed and stem volume determined using a taper function derived from tri-dimensional stem models.This reference volume was compared with the results of formulaic estimations.Tree stem profiles were further decomposed into different portions,approximately corresponding to the stump,butt logs and logs,and the suitability of each solid of revolution was assessed for simulating the resulting shapes.Stem volumes calculated using the fustrums of paraboloid and neiloid formulae were the closest to reference volumes with a bias and root mean square error of 8.0%and 24.4%,respectively.Stems closely resembled fustrums of a paraboloid and a neiloid.Individual stem portions assumed different solids as follows:fustrums of paraboloid and neiloid were more prevalent from the stump to breast height,while a paraboloid closely matched stem shapes beyond this point.Therefore,a more accurate stem volumetric estimate was attained when stems were considered as a composite of at least three geometric solids. 展开更多
关键词 Structure from motion photogrammetry Point cloud data Stem volume Savanna species Benin
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Progress and perspectives of point cloud intelligence 认领 引用 被引量：1
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作者 Bisheng Yang Nobert Haala Zhen Dong 《Geo-Spatial Information Science》 SCIE EI CSCD 2023年第2期189-205,共17页
With the rapid development of reality capture methods,such as laser scanning and oblique photogrammetry,point cloud data have become the third most important data source,after vector maps and imagery.Point cloud data ... With the rapid development of reality capture methods,such as laser scanning and oblique photogrammetry,point cloud data have become the third most important data source,after vector maps and imagery.Point cloud data also play an increasingly important role in scientific research and engineering in the fields of Earth science,spatial cognition,and smart cities.However,how to acquire high-quality three-dimensional(3D)geospatial information from point clouds has become a scientific frontier,for which there is an urgent demand in the fields of surveying and mapping,as well as geoscience applications.To address the challenges mentioned above,point cloud intelligence came into being.This paper summarizes the state-of-the-art of point cloud intelligence,with regard to acquisition equipment,intelligent processing,scientific research,and engineering applications.For this purpose,we refer to a recent project on the hybrid georeferencing of images and LiDAR data for high-quality point cloud collection,as well as a current benchmark for the semantic segmentation of high-resolution 3D point clouds.These projects were conducted at the Institute for Photogrammetry,the University of Stuttgart,which was initially headed by the late Prof.Ackermann.Finally,the development prospects of point cloud intelligence are summarized. 展开更多
关键词 Point cloud big data point cloud intelligence semantic labeling structured modeling machine learning
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融合点云与结构面重建的隧道失稳块体判识及靶向支护 认领 引用 被引量：1
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作者 闫志强 肖喜 +4 位作者 田四明 赵瑞杰 姚汝冰 贺鹏 石少帅 《隧道建设（中英文）》 EI CSCD 北大核心 2026年第4期751-765,共15页
为精准判识节理岩体隧道施工中的潜在失稳块体并做出针对性支护方案,提出一种融合点云与结构面重建的失稳块体判识及针对性支护方法。首先,通过三维激光扫描获取隧道掌子面点云数据,采用基于DPC(density peaking clustering)改进的DBSCA... 为精准判识节理岩体隧道施工中的潜在失稳块体并做出针对性支护方案,提出一种融合点云与结构面重建的失稳块体判识及针对性支护方法。首先,通过三维激光扫描获取隧道掌子面点云数据,采用基于DPC(density peaking clustering)改进的DBSCAN(density-based spatial clustering of applications with noise)算法(DPC-DBSCAN),实现结构面的自适应聚类与信息提取,克服传统方法中参数依赖人工调试、效率低、精度不足的问题;然后,对获取的结构面参数实现DFN(discrete fracture network)重建,结合现场地应力与围岩力学参数,构建合成岩体模型(SRM),并利用三维离散元程序(3DEC)模拟隧道开挖过程,实现潜在失稳块体的三维动态识别与稳定性评价;最后,以安徽西武岭隧道为依托,提出并对比6种支护方案,明确靶向支护的关键参数与实施策略。研究表明:1)提出的DPC-DBSCAN算法在结构面聚类中表现出更高的效率与精度,其运行时间较传统算法减少约38%,有效提升了结构面信息提取的自动化程度与可靠性;2)基于点云提取的结构面信息参数与现场人工测量结果吻合良好,验证了该智能识别方法的正确性与工程适用性;3)针对失稳块体提出的靶向支护方案,通过局部加长锚杆至5 m,加密环距至0.6 m,并优化锚杆角度,使失稳块体最大位移降至1.1 mm,显著提升了支护效果,同时在保证安全的前提下降低了材料用量,具备良好的工程经济性。 展开更多
关键词 隧道 点云数据 岩体结构面 块体失稳 靶向支护
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高精度三维激光引导的装配式钢结构智能动态焊接方法研究 认领 引用 被引量：1
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作者 苏相岗 王坤 +6 位作者 黄敏 黄乐华 陆欢 王杰惠 易天琦 党隆基 王元清 《工业建筑》 CAS 2026年第1期176-184,共9页
装配式钢结构因其良好的施工便捷性,在现代建筑工程中得到广泛应用。然而,传统的焊接工艺在钢结构的连接过程中面临焊接精度低、自动化水平不足、施工质量受人为因素影响较大等问题,严重制约了装配式建筑的智能化和高效化发展。为此,提... 装配式钢结构因其良好的施工便捷性,在现代建筑工程中得到广泛应用。然而,传统的焊接工艺在钢结构的连接过程中面临焊接精度低、自动化水平不足、施工质量受人为因素影响较大等问题,严重制约了装配式建筑的智能化和高效化发展。为此,提出了一种基于高精度三维激光引导的装配式钢结构智能动态焊接方法,以提升焊接精度,减少人为误差,并实现焊接过程的智能化控制。本方法利用高精度三维激光扫描技术获取焊接区域的空间特征,通过点云数据处理与智能识别算法,自动识别焊缝位置及形态,进而精确规划焊接轨迹。结合机器人视觉引导技术与自适应控制算法,系统能够实时调整焊接参数,以适应焊缝间隙、构件公差及施工环境变化,提高焊接精度和一致性。同时,引入深度学习模型对焊接过程进行智能监测与缺陷检测,通过焊缝成形质量分析和焊接缺陷识别,实现焊接质量的实时评估和优化调整。为验证该方法的有效性,搭建了高精度三维激光扫描与智能焊接试验平台,并在不同工况下进行了焊接精度、焊接效率及焊接质量的测试分析。试验结果表明,该方法能够显著提高焊缝定位精度,减少焊接变形,提高焊接强度与一致性,并有效降低施工成本和人工干预需求。相比传统焊接方式,本研究提出的方法在装配式钢结构施工中展现出了更高的智能化水平和更优的工程适用性,为装配式建筑智能建造提供了一种高效、可靠的焊接解决方案。 展开更多
关键词 高精度三维激光 钢结构 智能焊接 点云数据处理 机器人视觉引导
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基于PointCNN的煤场煤堆点云识别与体积计算 认领 引用
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作者 费亦凡 张豪庆 俞更喜 《内蒙古电力技术》 2025年第4期95-100,共6页
针对人工盘煤成本高昂与激光测量方法精度受限等问题,提出基于PointCNN网络的煤场煤堆点云识别与体积计算方法。首先,利用欧式距离对毫米波雷达获取的煤堆原始点云数据进行分割;其次,采用PointCNN网络精确识别目标煤堆点云数据,并采用De... 针对人工盘煤成本高昂与激光测量方法精度受限等问题,提出基于PointCNN网络的煤场煤堆点云识别与体积计算方法。首先,利用欧式距离对毫米波雷达获取的煤堆原始点云数据进行分割;其次,采用PointCNN网络精确识别目标煤堆点云数据,并采用Delaunay三角剖分算法及投影法实现煤堆点云数据的三维曲面重建和煤堆的体积计算;最后,以某燃煤电站煤场为研究对象,对所提方法进行验证。结果表明,相较于传统测量方法,本文所提方法精度更高,相对误差低于5%,能够满足燃煤电站对煤场煤堆的体积测量要求。 展开更多
关键词 煤堆 点云数据 毫米波雷达 欧式距离 PointCNN网络 Delaunay三角剖分 投影法
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基于激光扫描的羊体点云数据三维重构的研究 认领 引用
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作者 周艳青 白云莉 +3 位作者 乌丹牧其尔 白洁 马学磊 姜新华 《应用激光》 CSCD 北大核心 2026年第6期234-241,共8页
针对传统羊体尺参数测量存在的工作量大、精度低、应激反应强等问题,利用逆向工程技术,提出一种基于羊体三维重构的体尺参数测量方法。其次,利用激光三维扫描仪采集羊体点云数据。其次,由于离群噪声的存在,故利用基于改进的K-近邻搜索... 针对传统羊体尺参数测量存在的工作量大、精度低、应激反应强等问题,利用逆向工程技术,提出一种基于羊体三维重构的体尺参数测量方法。其次,利用激光三维扫描仪采集羊体点云数据。其次,由于离群噪声的存在,故利用基于改进的K-近邻搜索算法对点云数据进行去噪处理。为提高后续处理的效率,接着采用八叉树编码算法精简数据,获得均匀分布的点云数据,且保留羊体特征。最后,利用Delaunay三角化算法建立羊体曲面模型,进而提取羊的7种体尺参数。结果表明,利用杰魔软件测量的体尺参数平均相对误差为1.07%,而利用所提算法测量的体尺参数平均相对误差为1.46%,所提算法的误差略高于杰魔软件。同时,将所提算法与基于双目立体视觉三维重构提取的5种体尺参数测量值进行比较,二者的平均相对误差分别是1.51%和3.47%,更进一步说明所提算法检测的准确性。 展开更多
关键词 羊体尺参数 点云数据 数据去噪 数据精简 三维重构
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三维激光扫描技术在水工隧洞变形监测中的应用 认领 引用 被引量：2
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作者 李亚飞 丁国章 +1 位作者 王安 缪盾 《水利水电快报》 2026年第2期61-64,98,共4页
为探讨三维激光扫描技术在水工隧洞变形监测中的应用,解决传统监测方法在复杂三维空间中的局限性,提升监测精度和效率,以浙江省缙云县潜明水库引水工程黄坛隧洞为研究对象,采用三维激光扫描技术采集多期高精度点云数据,通过构建切面中... 为探讨三维激光扫描技术在水工隧洞变形监测中的应用,解决传统监测方法在复杂三维空间中的局限性,提升监测精度和效率,以浙江省缙云县潜明水库引水工程黄坛隧洞为研究对象,采用三维激光扫描技术采集多期高精度点云数据,通过构建切面中心点并进行中心线对比分析,系统研究隧洞的变形特征。结果表明:两处切面位置的几何中心在水平和垂直方向上相对变化量的绝对值均不超过1 mm,各期隧洞中心线与第一期中心线在各方向上的偏角绝对值均控制在10 arcsec以内,对应的位移偏差不超过1 mm。三维激光扫描技术能够实现大范围、高精度的隧洞变形监测,有效克服了传统方法的局限性,为水工隧洞变形监测提供技术支撑。 展开更多
关键词 三维激光扫描 水工隧洞 变形监测 点云数据 中心线分析
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基于LM算法的三维点云与二维图像标定方法 认领 引用
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作者 吴龙 陶奕帆 +2 位作者 杨旭 徐璐 陈淑玉 《现代电子技术》 北大核心 2026年第1期59-65,共7页
针对激光雷达与相机检测时标定精度不足,导致后续激光雷达点云与相机图像的空间对齐产生误差,影响后续特征匹配、物体检测和三维重建准确性的问题,文中提出一种基于激光雷达三维点云和单目相机的二维图像的标定方法,旨在实现对大规模物... 针对激光雷达与相机检测时标定精度不足,导致后续激光雷达点云与相机图像的空间对齐产生误差,影响后续特征匹配、物体检测和三维重建准确性的问题,文中提出一种基于激光雷达三维点云和单目相机的二维图像的标定方法,旨在实现对大规模物体的精确检测和三维环境重建。该方法首先通过多帧点云数据叠加获得相对密集的点云测量,并利用角点检测算法检测图像中的特征角点;随后使用偏最小二乘法(PLS)对参数进行求解;最后利用LM迭代算法最小化重投影误差,提高标定精度。标定结果表明,SPAAM算法相较于经典方法重投影误差减少8.6%,所提方法相较于经典方法重投影误差减少近38.2%,验证了所提方法的准确性和有效性。 展开更多
关键词 激光雷达 单目相机 标定方法 点云数据 偏最小二乘法 LM迭代算法
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