Search Results (101)

Accurate extraction of transmission towers from Unmanned Aerial Vehicle (UAV) Light Detection and Ranging (LiDAR) point clouds is a prerequisite for overhead transmission line (OTL) acceptance. This task remains challenging because tower points are heavily entangled with ground, vegetation, conductors, and insulators, especially in complex terrain. To address this issue, we propose a shape prior-guided coarse-to-fine framework for tower extraction from UAV LiDAR point clouds. First, candidate tower regions are localized from the scene point cloud through preprocessing, near-ground suppression, and density-based clustering. Second, the least-disturbed central body of each candidate tower is identified in a slice-wise manner and used to estimate the tower orientation and four principal structural axes. Third, side-view and front-view structural envelopes are progressively inferred to suppress non-tower points around the tower body and tower head. Finally, a base-constrained filtering strategy is introduced to remove residual ground and low-vegetation points within the tower footprint. Experiments conducted on multiple OTL datasets acquired in different regions of China, including plains and mountainous areas, demonstrate that the proposed method achieves robust and efficient tower extraction across diverse scenarios. The results indicate that explicit structural priors offer a promising complement to feature-driven and data-intensive approaches, particularly in scenarios with limited annotated data and strict real-time requirements. The proposed method processes scene point clouds containing tens to hundreds of millions of points, with an average extraction time of approximately 100 to 300 s per tower depending on scene density. Full article

Mechanical, electrical, and plumbing (MEP) pipe installation discrepancies can increase rework, complicate inspection, and affect subsequent operation and maintenance. This study presents a 3D laser scanning and Building Information Modeling (BIM)-based workflow for localizing and preliminarily classifying MEP pipe installation discrepancies in a building project. Preprocessed scanned pipe point clouds are registered with BIM-derived pipe point clouds through a coarse-to-fine Scan-BIM registration process. Individual pipe instances are extracted using distance-threshold-based growing, and scan-to-BIM pipe correspondence is established using nearest-neighbor root mean square error (RMSE). Pipes with relatively large overall RMSE values are further divided into slices to identify local high-discrepancy intervals. A slice-level discrepancy distribution function $R\left(s\right)$, together with derivative-magnitude and derivative-fluctuation thresholds, is used to support preliminary Type 1/Type 2 interpretation of representative discrepancy patterns. In a student dormitory case, the workflow screened local pipes with relatively large discrepancies, localized maximum-RMSE regions, and distinguished representative connection-related discrepancies from overall offset or inclination cases. A threshold perturbation check showed consistent Type 1/Type 2 labels for the four representative cases within the tested range. The workflow provides case-study evidence for localized MEP pipe inspection, while broader validation across projects and pipe systems remains necessary. Full article

To address the issues of low efficiency and large errors in traditional dimensional measurement strategies for fuel nozzles, this paper proposes an improved region-constrained Random Sample Consensus (RANSAC) circle fitting method for high-precision measurement of the inner hole cone angle. Three-dimensional point clouds are extracted using a shape-from-focus method. The point cloud slices are then projected onto a two-dimensional plane, and the slice edges are extracted. Based on the edge shape distribution, the candidate point selection strategy of RANSAC is optimized: the initial circle is divided into eight sector regions, and three points are randomly selected from three distinct regions to fit candidate circles. After multiple iterations, the optimal fitting circle is obtained. A comparative analysis is conducted among the least squares method, standard RANSAC, and the proposed algorithm, with three quantitative metrics—residual standard deviation (σ), root mean square error (RMSE), and inlier ratio (ε)—introduced to evaluate the fitting quality. Experimental results show that the proposed region-constrained RC-RANSAC method achieves the best performance among the three, yielding σ = 2.826 px, RMSE = 2.826 px, and ε = 95.2%, and attains a cone angle deviation of only 1.0°, which closely agrees with Keyence ultra-depth measurements (error 0.8°). This method provides a new approach for accurate and robust cone angle measurement of fuel nozzle inner holes. Full article

In satellite-denied environments, conventional localization methods struggle with rapid pose initialization due to the absence of global positioning data. To address this challenge, this study presents a high-precision pose initialization framework based on a Siamese Neural Network (SNN) and multi-stage probabilistic registration localization. First, the SNN improved by Convolutional Block Attention Module (CBAM) matches features between real-time radar point clouds and prior map slices, producing candidate positions based on similarity scores. Then, Adaptive Monte Carlo Localization (AMCL) performs probabilistic matching among these candidates to identify the correct slice and refine the position accuracy from tens of meters to meter-level, along with an approximate orientation estimate. Finally, the Normal Distributions Transform (NDT) is applied for point cloud registration, achieving centimeter-level pose estimation. The proposed method is evaluated on self-collected medium-scale and large-scale maps. Experimental results show that the SNN effectively identifies the correct map slice, which is further refined by AMCL and NDT to achieve centimeter-level position accuracy and sub-degree orientation accuracy. The multi-stage method achieves localization success rates of 99% on both 200 × 100 m and 300 × 200 m regions, with distance RMSEs of 0.175 m and 0.348 m, and orientation RMSEs of 0.149° and 0.437°, respectively. Evaluations on the KITTI dataset further demonstrate robust initialization performance in complex outdoor environments. The proposed framework provides a reference for high-precision pose initialization in large-scale satellite-denied scenarios. Full article

Accurate stockpile volume estimation is crucial for material quantification and inventory management in civil engineering, directly affecting cost assessment and on-site decision-making. Traditional manual methods suffer from subjective bias and limitations in handling irregular geometries, resulting in reduced accuracy and efficiency. This study presents a Light Detection and Ranging (LiDAR)-based workflow integrated with Robot Operating System (ROS) for point cloud processing, enabling accurate volume estimation of irregular stockpiles. The core innovation lies in the integration of multi-station scanning, point cloud registration, boundary extraction, layered slicing, and numerical integration using the trapezoidal rule, thereby enabling geometrically precise volume estimation of irregular stockpiles. The proposed system was validated through three experimental scenarios: (1) controlled experiments, showing strong agreement with theoretical volumes; (2) verification experiments, demonstrating high stability and consistency; and (3) field experiments, yielding a volume of 124.93 m³ compared to 130–135 m³ obtained by manual measurement. The results indicate that the proposed approach reduces processing time by over 80% while significantly decreasing labor requirements and improving operational safety. Overall, the proposed method provides a reliable and efficient solution for volume estimation in practical engineering applications. Full article

In the field of subgrade compaction quality assessment, accurate volume measurement of excavated pits is hindered by non-uniform point cloud distribution, environmental noise interference, and complex irregular boundary features. To address these challenges, this paper proposes a robust volume detection framework that integrates adaptive point cloud refinement and morphological discrimination. First, a pose normalization method employing RANSAC plane fitting and rigid body transformation corrects the spatial orientation of the raw point clouds. To balance data redundancy removal with feature preservation, a gradient adaptive simplification strategy based on local density feedback and K-nearest neighbor estimation is developed. Subsequently, a cross-sectional area calculation model utilizing piecewise-cubic polynomial fitting is proposed to mitigate boundary noise and accurately reconstruct irregular contours. Furthermore, a dynamic outlier removal mechanism based on the Median Absolute Deviation (MAD) and sliding windows is introduced to eliminate non-physical geometric fluctuations. Finally, the total volume is aggregated using a hybrid strategy of Simpson’s rule and a frustum compensation operator. Experimental results on simulated pits with typical topological defects demonstrate that the proposed algorithm outperforms traditional methods, achieving an average relative volume error of less than 0.8%. This approach significantly improves the robustness and precision of sensor-based automated subgrade compaction quality measurement. Full article

This paper presents a computationally feasible/time-effective Scan-to-BrIM workflow for generating a highly detailed digital model of a complex steel pedestrian bridge. The proposed methodology integrates rapid and accurate point cloud acquisition with advanced parametric modelling and structural information management. First, a high-resolution point cloud is produced using a fast survey strategy that ensures the geometric precision required for a faithful representation of the existing structure. Second, the point cloud is processed in Rhinoceros/Grasshopper, where a custom Python (version 3.13) algorithm automatically detects and generates reference planes containing the structural components, enabling the creation of a consistent and fully parametric BrIM model. The latter approach includes metric normalization, voxel-based downsampling, reliable under tested conditions ground and outlier removal, and PCA (Principal Component Analysis)-based reorientation, followed by guided slicing of the point cloud and projection of each slice onto its section plane. The proposed workflow achieved a geometric RMSE of 2.5 mm with a total processing time of 7.3 h. The resulting parametric model achieves geometric consistency with the source point cloud within an operational tolerance range of approximately 5–10 mm, in line with the requirements of structural applications. Finally, the model is organised and managed within the BrIM environment and then transferred to a downstream FEM environment for preliminary structural application. The workflow is tested on a case study of a 40-m steel pedestrian bridge located in central Italy. Results demonstrate that the integrated approach provides a reproducible and semi-automated solution that reduces manual intervention in Scan-to-BrIM processes for producing accurate parametric models of steel pedestrian bridges, supporting structural assessment, asset management, and future maintenance strategies. Full article

Instance segmentation of plant point clouds is challenging due to intricate structures, non-uniform density, and large intra-class variation. Conventional methods often suffer from blurred boundaries, instance adhesion, and insufficient coupling of semantic and instance features. To address these issues, this paper proposes MPRSF-CSA, a novel network integrating recursive slice-based feature extraction with an attention-embedding mechanism. The method first transforms disordered point clouds into ordered sequences via a multi-directional recursive slicing strategy and models inter-slice dependencies using BiLSTM. Parallel decoding branches for semantic and instance segmentation are constructed, and a core attention-embedding module facilitates bidirectional fusion of semantic and instance features. Instance segmentation is achieved via clustering and semantic-aware optimization. Experiments on two public datasets demonstrate that MPRSF-CSA outperforms existing approaches in segmentation accuracy, boundary preservation, and adaptability to complex plant scenes. Full article

Event cameras are bio-inspired vision sensors offering low latency, low power consumption, and high dynamic range, capturing motion with microsecond-level precision via a per-event triggering mechanism. Despite these advantages, the inherent sparsity and lack of color in event data hinder direct analysis, necessitating advanced deep learning approaches. To achieve low-latency and high-precision motion segmentation for indoor robotic applications, this paper introduces a dual-branch decoupled CNN framework. Specifically, Principal Component Analysis (PCA) is utilized to project 3D event point clouds into 2D motion trend maps, capturing local motion priors while suppressing ambiguity in structured environments. Concurrently, an Event Leaky Integration (ELI) model, inspired by biological membrane potentials, is designed to enhance the structural representation of sparse events. Within this framework, separate branches respectively perform motion validation and shape extraction and are fused via a Spatial Gated Fusion (SGF) module to suppress static background interference. It is demonstrated experimentally that with an input window of only 10 ms, the proposed method achieves a 77% average mIoU across five indoor test scenarios from the EV-IMO dataset with an inference latency of 10 ms per frame. Compared to state-of-the-art methods like MSRNN and GCN, which required 30–300 ms event slices, our framework achieves a favorable trade-off between computational efficiency and segmentation accuracy, maintaining competitive performance under ultra-short time windows for indoor event-based motion processing. Full article

Deformation monitoring of roadway surrounding rock is the key link to ensure the safety production of the coal mine. The traditional monitoring method can only obtain the displacement information of discrete measuring points, and it is difficult to fully reflect the spatial distribution characteristics and evolution law of surrounding rock deformation. Based on the engineering background of the extra-thick coal seam roadway in the Yushupo Coal Mine, Shanxi Province, China, this study proposes a set of full-space deformation monitoring methods for roadway surrounding rock based on explosion-proof mobile 3D laser scanning technology. Firstly, a hierarchical denoising method based on improved statistical filtering is established. The quality of point cloud data is effectively improved by region clipping, a connectivity analysis guided by multi-dimensional geometric features and adaptive density threshold three-level processing strategy. Secondly, a hierarchical point cloud registration method combining physical anchor geometric constraints and deep learning patch guided matching is proposed to reduce the registration error to millimeter level. Finally, the deformation evaluation of surrounding rock is carried out by combining the overall deformation identification with the quantitative analysis of local section slices. The engineering application results show that the deformation of the roadway floor is the most significant during the monitoring period, the maximum deformation is 90.0 mm, and the average deformation is 46.9 mm. The maximum deformation of the roof is 35.0 mm, and the convergence of both sides is asymmetric. Compared with the total station, the results show that the maximum displacement error in each direction does not exceed 5 mm, and the standard deviation is within 1.3 mm, which meets the engineering accuracy requirements of coal mine roadway deformation monitoring. This study provides a complete technical scheme for panoramic and high-precision monitoring of surrounding rock deformation in coal mine roadway. Full article

Robotic polishing in CAD-free industrial settings relies on point-cloud data, yet noise and non-uniform sampling often compromise kinematic feasibility and finishing quality. This paper proposes an adaptive motion planning approach with explicit kinematic constraints. A downsampling–clustering–mapping-back strategy is first employed for rapid workpiece extraction. Subsequently, an improved supervoxel representation and attributed adjacency graph (AAG) are developed, utilizing a multi-objective energy formulation to partition sub-regions that satisfy geometric consistency and kinematic reachability. To handle point-cloud noise, a lightweight neural network predicts scanning directions and step-distance coefficients, followed by thick-slice serpentine path generation. Finally, closed-loop verification ensures safety through inverse-kinematics and safety-margin checks. Experimental results demonstrate consistent sub-micron finishing quality, with Ra ≈ 0.6 μm on complex mold surfaces. Moreover, the proposed pipeline achieves a 7.5× preprocessing speedup, completing workpiece extraction in 1.14 s for a 237,640-point scan, and improves kinematic feasibility to 100% IK success while reducing the mean TCP normal deviation by ~76% compared with a PCA-based baseline. Full article

Accurate extraction of the centroid axes of beams with variable cross-sections is critical for infrastructure health monitoring. While 3D laser scanning provides dense point clouds, existing methods face challenges due to fixed slicing directions, sparse or incomplete boundaries, and inaccurate centroid calculations for concave sections. This study proposes a robust framework to overcome these issues. An improved k-d tree ordering algorithm enhances boundary extraction through starting point constraint strategy and dynamic isolated noise point removal mechanism. A ray casting-based boundary-constrained Delaunay triangulation centroid calculation algorithm accurately computes centroids for arbitrary shapes, including concave profiles. An innovative convex hull centroid-driven adaptive normal iterative slicing method dynamically adjusts orientation using historical centroid data, aligning with the local member axis to minimize errors in variable or deformed regions. Experimental validation shows the method outperforms traditional fixed-direction slicing in effectiveness, parameter sensitivity, and deformation robustness, achieving sub-millimeter accuracy. Applied to monitor ultra-high-performance concrete cantilever beams at the Shanghai Grand Opera House, it produced centroid axis data consistent with total station measurements (differences within ±1.2 mm), supporting phased deformation warnings and safety assessments. This work provides a systematic, high-precision solution for extracting geometric axes from complex structural point clouds. Full article

To address the precision and motion-smoothing challenges in path planning for spherical workpieces without Computer-Aided Design (CAD) models, this paper proposes a robust point-cloud-driven framework. Conventional Principal Component Analysis (PCA) alignment suffers from centroid shift errors due to asymmetric data loss from light-absorbing surface features. To solve this, a RANSAC-compensated hybrid PCA algorithm is developed to decouple position and orientation estimation, ensuring stable coordinate alignment despite incomplete data. Furthermore, to resolve the geometric collapse and kinematic jitter caused by traditional planar slicing in high-curvature polar regions, a spherical latitudinal equiangular conical slicing algorithm is introduced. By aligning the slicing planes with the sphere’s radial geometry, the method preserves topological accuracy while maintaining an optimal point density for smooth robotic execution. Experimental results on rubber ball groove processing demonstrate a repeat positioning accuracy of 0.09 mm and a feature coverage of 95.21%. This research provides a scientifically rigorous and computationally efficient solution for the automated processing of complex spherical surfaces. Full article

Conducting regular verticality inspections for thin tower structures is essential for ensuring structural safety, extending service life, and optimizing operation and maintenance strategies. However, the traditional theodolite inspection method, as a commonly used technique for verticality assessment, still has certain limitations, including strict requirements for station setup, the need for high-altitude contact-based operations, and difficulty in accurately resolving the tilt azimuth of the central axis. More importantly, the conventional method provides insufficient understanding of the overall verticality geometric characteristics of thin tower structures, particularly lacking in systematic approaches for characterizing the axis morphology under non-contact, full three-dimensional (3D) perception conditions. Therefore, this study proposes a refined method for inspecting the verticality of thin tower structures using the Marching Square algorithm. The tower body of a tower crane was selected as the experimental subject. Firstly, ground-based LiDAR was employed to scan and acquire the raw point cloud data of the tower crane. After point cloud registration and denoising, high-precision and valid point cloud data of the tower body were obtained. Secondly, a cross-sectional slicing segmentation strategy was designed for the point cloud of the tower body standard sections, and a slice-polygon-contour extraction method based on the Marching Square algorithm was proposed to extract the contour vertices and compute the coordinates of the contour centroids. Finally, a spatial line-fitting algorithm based on the least squares method was proposed to fit a 3D line to the coordinates of the contour centroids, thereby determining the direction vector of the central axis. The direction vector was then subjected to vector operations with the x-axis and z-axis in the station-center space coordinate system to derive the tilt azimuth and tilt angle of the central axis, thereby providing the verticality inspection results of the tower crane. The experimental results indicate that the four cross-section slicing segmentation schemes designed using the proposed method in this study yielded tower crane verticality values of 2.45‰, 2.35‰, 2.20‰, and 2.18‰. All verticality values meet the verticality requirement of no more than 4‰ specified in GB/T 5031-2019 (Tower Cranes). This verifies that the proposed method is feasible and effective, providing a novel, high-precision, and non-contact inspection method for inspecting the anti-overturning stability of thin tower structures. Full article

In medical 3D printing, accuracy is critical for fabricating patient-specific implants and anatomical models. Although printer performance has been widely examined, the influence of slicing software on geometric fidelity is less frequently quantified. The slicing step, which converts STL files into printer-readable G-code, may introduce deviations that affect the final printed object. To quantify slicer-induced G-code deviations by comparing G-code-derived geometries with their reference STL modelsTwenty mandibular models were processed using five slicers (PrusaSlicer (version 2.9.1.), Cura (version 5.2.2.), Simplify3D (version 4.1.2.), Slic3r (version 1.3.0.) and Fusion 360 (version 2.0.19725)). A custom Python workflow converted the G-code into point clouds and reconstructed STL meshes through XY and Z corrections, marching cubes surface extraction, and volumetric extrusion. A calibration object enabled coordinate normalization across slicers. Accuracy was assessed using Mean Surface Distance (MSD), Root Mean Square (RMS) deviation, and Volume Difference. MSD ranged from 0.071 to 0.095 mm, and RMS deviation from 0.084 to 0.113 mm, depending on the slicer. Volumetric differences were slicer-dependent. PrusaSlicer yielded the highest surface accuracy; Simplify3D and Slic3r showed best repeatability. Fusion 360 produced the largest deviations. The slicers introduced geometric deviations below 0.1 mm that represent a substantial proportion of the overall error in the FDM workflow. Full article