Laser Point Cloud Research Articles

Methodology for retrospectively developing a BIM model from point cloud scans using ongoing building project as case study

Purpose The purpose of this study is to explore the suggestions that construction processes could be considerably improved by integrating building information modelling (BIM) with 3D laser scanning technologies. This case study integrated 3D laser point cloud scans with BIM to explore the effects of BIM adoption on ongoing construction project, whilst evaluating the utility of 3D laser scanning technology for producing structural 3D models by converting point cloud data (PCD) into BIM. Design/methodology/approach The primary data acquisition adopted the use of Trimble X7 laser scanning process, which is a set of data points in the scanned space that represent the scanned structure. The implementation of BIM with the 3D PCD to explore the precision and effectiveness of the construction processes as well as the as-built condition of a structure was precisely captured using the 3D laser scanning technology to recreate accurate and exact 3D models capable of being used to find and fix problems during construction. Findings The findings indicate that the integration of BIM and 3D laser scanning technology has the tendency to mitigate issues such as building rework, improved project completion times, reduced project cost, enhanced interdisciplinary communication, cooperation and collaboration amongst the project duty holders, which ultimately enhances the overall efficiency of the construction project. Research limitations/implications The acquisition of data using 3D laser scanner is usually conducted from the ground. Therefore, certain aspects of the building could potentially disturb data acquisition; for example, the gable and sections of eaves (fascia and soffit) could be left in a blind spot. Data acquisition using 3D laser scanner technology takes time, and the processing of the vast amount of data acquired is laborious, and if not carefully analysed, could result in errors in generated models. Furthermore, because this was an ongoing construction project, material stockpiling and planned construction works obstructed and delayed the seamless capture of scanned data points. Originality/value These findings highlight the significance of integrating BIM and 3D laser scanning technology in the construction process and emphasise the value of advanced data collection methods for effectively managing construction projects and streamlined workflows.

A high-accuracy multi-temporal laser point cloud registration method for providing a detailed expression of the urban environment

In consideration of the significant changes in urban environmental scenes and the impact of factors such as signal obstruction on positioning, the spatial positions of multi-temporal Mobile Laser Scanning (MLS) point clouds collected in the same area are inconsistent, which makes it challenging to provide a complete expression of the urban road environment. To address this issue, a precise registration algorithm based on pole-like targets is proposed. The core steps include registration primitive extraction: a cylindrical spatial neighborhood is established based on the distinctive key points, and precise extraction of pole-like geospatial point cloud data is performed using the Euclidean clustering method with the normal curvature constraint. Achieving optimal spatial transformation: a circle center fitting method with additional Mean Absolute Error (MAE) loss function constraints was developed based on the stable elemental unit which was utilized to accurately construct homonymous feature point pairs. Validation results from multiple real urban point cloud datasets collected from different experimental areas and platforms demonstrate that our method achieves superior registration performance, with mean residuals below 5 cm in both the planar and elevation directions. The proposed method exhibits strong universality and registration robustness for multi-temporal point cloud data of the homologous or cross-sources.

Measurement of combined gap in whole process of transmission lines' live working based on 3D laser point cloud

Measurement of combined gap in whole process of transmission lines' live working based on 3D laser point cloud

Semantic Segmentation of Large‑Scale Laser Point Cloud in Mines Based on Local Feature Enhancement

Semantic Segmentation of Large‑Scale Laser Point Cloud in Mines Based on Local Feature Enhancement

Research on the digital protection and three-dimensional modeling technology of ancient buildings

Abstract The starting point of ancient architecture digitization and 3D reconstruction research is data acquisition. Thus, this paper mainly analyzes the processing of 3D laser scanning data and the construction accuracy of 3D reconstruction models of ancient buildings based on point cloud data. This paper discusses the basic theory of point cloud data and the preprocessing of point cloud data, including point cloud splicing, point cloud denoising, point cloud streamlining, point cloud segmentation, and other methods and applicability are discussed in detail. The boundary extraction algorithm based on RANSAC and the graph cut algorithm are utilized to obtain the main frame line of the building, and a proposed method for reconstruction based on multi-view projection is proposed. Classify laser point cloud samples from ancient buildings, calculate each wall color’s R, G, and B values, and obtain preliminary classifications of laser point cloud data categories. The RANSAC algorithm was used to extract the point cloud data plane extraction peak of the ancient tower building, combined with the three-dimensional modeling coordinate difference of the test points, to analyze the accuracy of the three-dimensional modeling technology of ancient buildings in this paper. The number of plane extraction points of the RANSAC algorithm is 9504, and the standard deviation is 0.0505, which is close to 0. The results are more accurate, and the extraction effect has a certain degree of superiority. The plane coordinate deviation of the reconstruction model of the ancient tower is mainly concentrated in the range of 0.10~0.40m, and the individual coordinate difference is more than 0.10m. The errors in the x -direction and y -direction are ±0.18m and ±0.23m, respectively, and it can be seen from the results of the error calculation that the three-dimensional reconstruction plan of the building proposed in this paper can complete the reconstruction of the three-dimensional model of the building with a high degree of accuracy.

Surface flatness measurement of medium-thick plate based on Laser point cloud

Surface flatness measurement of medium-thick plate based on Laser point cloud

3D Target Detection Algorithm of Laser Point Cloud Based on Artificial Intelligence

3D Target Detection Algorithm of Laser Point Cloud Based on Artificial Intelligence

Research and Application of Dynamic Tree Growth Cycle Prediction Technology Based on Transmission Channel Laser Point Cloud

Research and Application of Dynamic Tree Growth Cycle Prediction Technology Based on Transmission Channel Laser Point Cloud

Digitalization verification method of live distance of wire

The live distance verification of wire is very important for the safety and stability of the construction and operation of the power system. At present, the commonly used acceptance method is to complete it manually through manual measurement tools. However, because the parallel line between the wire and the hanging height of the wire is high, it is difficult to judge the live distance of the wire by manual measurement. This paper proposes that two methods of laser point cloud and image recognition are used to check the charged distance of the wire, which can effectively improve the acceptance efficiency and acceptance accuracy.

Design and realization of the digital protection platform of traditional intangible cultural heritage based on virtual reality technology

Abstract Using new design concepts and design methods to effectively strengthen the digital protection of traditional non-heritage culture is an important measure to promote the development of conventional non-heritage culture. In this paper, the digital protection needs of traditional non-heritage culture are sorted out, including the purpose of digital protection of non-heritage, the design level, and the feasibility of the application of virtual reality technology in the digital protection of non-heritage culture. A platform for the digital protection of non-heritage culture is constructed with the model of PHP technology + MySQL database + Apache server combined with virtual reality technology, and the immersion experience and display dissemination model is designed in this platform. In order to obtain accurate digital images of traditional non-heritage culture, this paper utilizes 3D laser point cloud acquisition technology to reconstruct non-heritage culture in 3D. It combines the ICP algorithm to enhance the point cloud alignment accuracy of digital images of non-heritage culture. Evaluation of the effectiveness of the traditional non-heritage culture digital protection platform proposed in this paper was done through performance tests and application practice. The results show that the number of concurrent users of the platform in this paper can reach the maximum throughput of about 94,300 per second in 2000, the point cloud alignment accuracy of the ICP algorithm has an error of only 0.304mm, and the SUS usability evaluation of the platform scores 80.4 points. The construction of a digital protection platform for non-heritage culture using virtual reality technology can provide users with immersive experience scenes.

RESEARCH ON LIDAR SLAM METHOD WITH FUSED POINT CLOUD INTENSITY INFORMATION

Abstract. Under the current trend of intelligence and automation, simultaneous positioning and mapping technology has become one of the research hotspots. The main problems of SLAM technology research are to improve the robustness of mapping and positioning, establish an efficient back-end optimization system, and improve the generalization of SLAM technology. This paper proposes to fuse the intensity information of the point cloud and the geometric information of the environment scene to construct a globally consistent environment feature descriptor and use the non-iterative two-step method to perform the nearest neighbour search on the point cloud in the point cloud registration stage. Then use the globally consistent descriptor that has been constructed to extract the laser point cloud descriptor by using the ring partition method, combine the method based on domain search to search for the closest point cloud frame, and finally use Intensity-ICP to complete the loopback frame. Fine registration, outputs the optimal pose transformation, to complete the loop detection. We use our self-built mobile platform to verify the robustness and generalization of the improved laser SLAM algorithm in public datasets and campus datasets. Experimental results show that the improved algorithm reduces the trajectory drift of the mobile platform and improves the efficiency of point cloud registration and loop closure detection.

Quantitative Detection Technology for Geometric Deformation of Pipelines Based on LiDAR.

This paper introduces a novel method for enhancing underground pipeline inspection, specifically addressing limitations associated with traditional closed-circuit television (CCTV) systems. These systems, commonly used for capturing visual data of sewer system deformations, heavily rely on subjective human expertise, leading to limited accuracy in detection. Furthermore, their inability to perform quantitative analyses of deformation extent hampers overall inspection effectiveness. Our proposed method leverages laser point cloud data and employs a 3D scanner for objective detection of geometric deformations in underground pipe corridors. By utilizing this approach, we enable a quantitative assessment of blockage levels, offering a significant improvement over traditional CCTV-based methods. The key advantages of our method lie in its objectivity and quantification capabilities, ultimately enhancing detection reliability, accuracy, and overall inspection efficiency.

Application of Airborne Radar Variable Altitude Aerial Survey in High Precision Mapping of Mountainous Areas

This paper studies how to use UAV aerial survey equipment to carry laser radar and high-definition real-scene lenses to achieve high-precision mountain mapping. The study aims to solve the problems of complex terrain, difficulties in surveying, and controlling the accuracy of surveying and mapping in mountainous, canyon, and hilly areas. Firstly, the LK tea production area development project is selected as an example for example analysis. The aerial survey equipment and data acquisition equipment are selected according to the characteristics of the project. Secondly, aiming at the problem that the mountainous terrain undulates greatly and that the accuracy of ground object mapping is difficult to control, this paper puts forward the “variable height aerial survey” method. It collects aerial survey data according to the terrain characteristics. Finally, this paper proposes a “point cloud overlay” modeling method. It combines laser point cloud data with real 3D data to form a high-precision real 3D model point cloud reorganization and establish a high-precision real 3D model.

A multi-sensor fusion framework with tight coupling for precise positioning and optimization

In the dynamic landscape of artificial intelligence and robotics, the pursuit of accurate positioning in mobile robots has intensified. This research addresses the limitations of single-sensor SLAM (Simultaneous Localization and Mapping) techniques in complex settings by harnessing the collective strengths of LiDAR (Light Detection And Ranging), Camera, IMU (Inertial Measurement Unit), and GNSS (Global Navigation Satellite System) sensors. The proposed multi-sensor tightly-coupled SLAM framework is an integration of point-line feature-based laser–visual–inertial odometry, visual–laser fusion loop closure detection, and factor graph-based back-end optimization. Within the visual–inertial subsystem, an advanced LSD (Line Segment Detector) feature extraction strategy is introduced, incorporating point-line fusion to enhance visual line features. Additionally, the laser point cloud is projected onto the camera coordinate system, establishing depth associations with visual attributes. Strengthening the robustness of the visual–inertial subsystem in low-texture environments, camera poses undergo optimization through a sliding-window bundle adjustment method. In the laser-inertial subsystem, IMU preintegration mitigates laser point cloud distortion. Extracting edge and plane features, coupled with frame-to-local-map matching, enhances matching efficiency while streamlining computational intricacies. This amalgamation forms the basis of the laser–visual–inertial odometry fusion system. To overcome the limitations of standalone visual and laser-based loop closure detection, a dual-loop closure method utilizing visual–laser fusion is proposed. Leveraging the DBoW2 bag-of-words model, complemented by temporal–spatial consistency checks, enhances detection efficiency and accuracy. The integration of GNSS factors imparts global constraints for expansive outdoor scenarios. Employing factor graph-based back-end optimization, the refinement of laser–visual–inertial odometry factors, visual–inertial odometry factors, IMU preintegration factors, loop closure factors, and GNSS factors culminates in precise global pose estimation and high-fidelity point cloud maps. Through rigorous evaluation of the M2DGR dataset and a mobile robot platform, the proposed methodology emerges as an exemplar of performance, showcasing superiority over the state-of-the-art LIO-SAM technique. Achieving a reduction of 2.86m and 3.23m in the root mean square error of absolute pose estimation across divergent environments, this approach exhibits remarkable efficacy in outdoor scenarios, thereby elevating the precision and resilience of SLAM algorithms for mobile robots.

Integrating vision and laser point cloud data for shield tunnel digital twin modeling

In shield tunnel digital twin modeling, constructing high-precision tunnel digital 3D models is essential but poses a challenging engineering problem that affects efficiency and accuracy. To address this, this paper presents a method based on 3D tunnel information models using image and point cloud data. A 3D Tunnel Information Model Construction Method (3DTIM) is proposed, based on vision and laser point cloud data, encompassing six core processing layers, and employing the YOLO-T tunnel inner wall image target recognition method alongside various point cloud data processing methods. Through this method, critical parameters can be rapidly and accurately extracted from images and point cloud data, enabling the direct creation of high-precision 3D tunnel models on the 3D modeling software with an accuracy better than 0.7 cm. The 3DTIM approach not only solves the problem of constructing high-precision tunnel 3D models, but also provides a valuable methodological reference for digital shield tunnel twin modeling, reducing the need for manual measurement and enhancing the efficiency of tunnel digital model construction. This research provides a new practical framework for future studies on tunnel 3D model construction and digital shield tunnel twin modeling based on image and point cloud data, forming a more valuable methodological reference.

Branching the limits: Robust 3D tree reconstruction from incomplete laser point clouds

Accurate individual tree reconstruction based on laser point clouds is vital for precise biomass estimation, virtual geographic environment modeling, and simulation. However, mobile laser 3D scanning systems often capture tree point clouds obscured by leaves, resulting in missing or incomplete branch data, posing significant challenges to detailed tree reconstruction. In this paper, a novel method for fine-grained 3D tree model reconstruction from incomplete point clouds is proposed. This approach addresses the structural reconstruction of trees, tackling two main problems: reconstructing the main trunk and branches based on data completeness. Initially, a 3D morphological algorithm is employed to separate the main trunk and branches in the point cloud. Next, branch point clouds are node-aggregated using clustering, and the trunk’s skeleton point is computed using a multi-scale curve fitting method. To account for incomplete branch point clouds, the Alpha shape is calculated and used as a constraint for the growth model of the L-system, enabling the automatic generation of branch structures. Finally, a morphologically constrained multi-level trunk fusion method is utilized to achieve complete tree model reconstruction. To validate the effectiveness of this method, five trees with varying structures and levels of complexity were selected for structural reconstruction and compared with state-of-the-art (SOTA) methods. Experimental results evince that the method delineated in this study exhibits an aptitude for adeptly approximating tree nodes, even in scenarios characterized by incomplete point cloud data. This approach transcends the reconstruction accuracy manifested by SOTA algorithms specific to three-dimensional tree modeling, simultaneously demonstrating a pronounced resilience to noise and enhanced robustness.

Indoor Space Modeling and Parametric Component Construction Based on 3D Laser Point Cloud Data

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.

Study on the 3D-printed surface defect detection based on multi-row cyclic scanning method

Defect detection is a crucial part of the additive manufacturing process. Based on 3D laser point cloud, this paper takes the surface of AM process as the research object for defect detection, including surface point cloud pretreatment and internal defect detection. Aiming at a kind of interference existing on the boundary in the process of AM, we use continuous constraint and improved slope method to deal with it, and discuss two cases of continuous surface and non-continuous surface. The small threshold based pass-through filtering algorithm extracts the external points of the defect boundary by using the fluctuation of the height value, and classifies the defect point cloud by classifying, combining and matching the external points. Finally, experimental verification results show that the accuracy of our proposed surface defect detection method can reach 92.3%. This method can effectively extract relatively complete defects. It provides important technical support for surface quality inspection in industrial automation.

PointDMS: An Improved Deep Learning Neural Network via Multi-Feature Aggregation for Large-Scale Point Cloud Segmentation in Smart Applications of Urban Forestry Management

Background: The development of laser measurement techniques is of great significance in forestry monitoring and park management in smart cities. It provides many conveniences for improving landscape planning efficiency and strengthening digital construction. However, capturing 3D point clouds in large-scale landscape environments is a complex task that generates massive amounts of unstructured data with characteristics such as randomness, rotational invariance, sparsity, and serious barriers. Methods: To improve the processing efficiency of intelligent devices for massive point clouds, we propose a novel deep learning neural network based on a multi-feature aggregation strategy. This network is designed to divide 3D laser point clouds in complex large-scale scenarios. Firstly, we utilize multiple terrestrial laser sensors to collect a large amount of data in open scenes such as parks, streets, and forests in urban environments. These data are integrated into a practical database called DMSdataset, which contains different information variables, densities, and dimensions. Then, an automatic block integrated with a multi-feature extractor is constructed to pre-process the unstructured point cloud data and standardize the datasets. Finally, a novel semantic segmentation framework called PointDMS is designed using 3D convolutional deep networks. PointDMS achieves a better segmentation performance of point clouds with a lightweight parameter structure. Here, “D” stands for deep network, “M” stands for multi-feature, and “S” stands for segmentation. Results: Extensive experiments on self-built datasets show that the proposed PointDMS achieves similar or better performance in point cloud segmentation compared to other methods. The overall identification accuracy of the proposed model is up to 93.5%, which is a 14% increase. Particularly for living wood objects, the average identification accuracy is up to 88.7%, which is, at least, an 8.2% increase. These results effectively prove that PointDMS is beneficial for 3D point cloud processing, division, and mining applications in urban forest environments. It demonstrates good robustness and generalization.

Utilizing Deep Learning-Based Fusion of Laser Point Cloud Data and Imagery for Digital Measurement in Steel Truss Member Applications

Utilizing Deep Learning-Based Fusion of Laser Point Cloud Data and Imagery for Digital Measurement in Steel Truss Member Applications