Alpha-shape Algorithm Research Articles

Combination of LiDAR detection and green integral method for calculating irregular cross-section geometric properties of deteriorated components in timber historic buildings

Timber components in historic buildings are susceptible to deterioration due to the complex external environment. The loss of material in these components and changes in their cross-section geometry significantly impact the stability and load-bearing capacity of historic buildings. A portable LiDAR device is utilized to acquire the cross-section geometry of the deteriorated components. The irregular cross-section profiles are extracted using point cloud slicing and the Alpha-Shape algorithm. Geometric characteristics such as cross-section area, moment of inertia, and moment of resistance are calculated using Green's integral method. The performance of this method is evaluated by measuring eight groups of deteriorated components with different cross-section characteristics and comparing it with five traditional methods. The results demonstrate that the proposed method improves the calculation accuracy by 1%–21% relative to traditional methods, thereby enhancing reliability by 9%–54%. Additionally, it reduces manual calculation workload and human error, while promoting calculation efficiency. The method exhibits excellent applicability and high efficiency, with an error below 3% when compared to the true value. This method proves valuable in evaluating the extent of deterioration in historic timber components and provides essential data for preventive conservation and restoration of historic timber buildings.

Multi-Feature-Filtering-Based Road Curb Extraction from Unordered Point Clouds

Road curb extraction is a critical component of road environment perception, being essential for calculating road geometry parameters and ensuring the safe navigation of autonomous vehicles. The existing research primarily focuses on extracting curbs from ordered point clouds, which are constrained by their structure of point cloud organization, making it difficult to apply them to unordered point cloud data and making them susceptible to interference from obstacles. To overcome these limitations, a multi-feature-filtering-based method for curb extraction from unordered point clouds is proposed. This method integrates several techniques, including the grid height difference, normal vectors, clustering, an alpha-shape algorithm based on point cloud density, and the MSAC (M-Estimate Sample Consensus) algorithm for multi-frame fitting. The multi-frame fitting approach addresses the limitations of traditional single-frame methods by fitting the curb contour every five frames, ensuring more accurate contour extraction while preserving local curb features. Based on our self-developed dataset and the Toronto dataset, these methods are integrated to create a robust filter capable of accurately identifying curbs in various complex scenarios. Optimal threshold values were determined through sensitivity analysis and applied to enhance curb extraction performance under diverse conditions. Experimental results demonstrate that the proposed method accurately and comprehensively extracts curb points in different road environments, proving its effectiveness and robustness. Specifically, the average curb segmentation precision, recall, and F1 score values across scenarios A, B (intersections), C (straight road), and scenarios D and E (curved roads and ghosting) are 0.9365, 0.782, and 0.8523, respectively.

Research on visualization of cotton canopy structure and extraction of feature parameters based on dual-perspective point cloud data

ABSTRACT Cotton is one of the crops that requires the most time and labor. Precision agriculture technology is required for efficient management of cotton, and the identification of cotton attribute information in the field is a necessary and crucial step towards implementing precision agriculture. Unmanned aerial vehicles (UAVs) and Light Detection and Ranging (LiDAR) have evolved into essential instruments for plant phenotyping research. In this study, in order to address the demand for cotton attribute identification over wide areas in the field, an airborne LiDAR system was built based on LiDAR detection technology. This work acquired a dual-view point cloud of a cotton field in order to address the high density and low accuracy of the cotton point cloud attributes. Following pre-processing of the data, the point cloud was first coarsely regenerated using a combination of Fast Point Feature Histograms (FPFH) and Intrinsic Shape Signatures (ISS) techniques. The dual-view point cloud registration was then refined and finished using an Iterative Closest Point (ICP) algorithm. The height of the cotton plant was determined using the reconstructed point cloud of the cotton canopy, and a method combining Graham’s algorithm and the Alpha-Shape algorithm was suggested to determine the porosity of the cotton layers. The findings revealed that the root mean square errors (RMSE) between calculated and measured values of cotton plant height and stratified porosity were, respectively, 3.98 cm and 5.21%, and that their mean absolute percentage errors (MAPE) were 4.39% and 9.31%, with correlation coefficients ( R 2 ) of 0.951 and 0.762, respectively. On the whole, our study has demonstrated the effectiveness of the proposed method in terms of providing accurate and reliable cotton parameters in agriculture.

Extraction of crop canopy features and decision-making for variable spraying based on unmanned aerial vehicle LiDAR data

Implementation of precision agriculture aerial applications using unmanned aerial vehicles (UAVs) represents a pivotal technological aspect in precision agriculture. However, current UAV applications are still far from realizing true precision variable spraying. The use of UAVs to analyze and model crop growth, pest and disease conditions, and other pertinent information to enable targeted precision spraying poses a formidable challenge. Herein, a UAV variable-rate spray system with adaptive flow control based on crop canopy morphology was developed using a UAV equipped with a light detection and ranging (LiDAR) sensor to support precision spraying of cotton. Point cloud data captured by the LiDAR sensor initially undergo processing using the simple morphological filter (SMRF) algorithm and feature extraction function. Subsequently, cotton canopy volumes measured by the alpha shape algorithm, convex hull by slices shape algorithm, and voxel-based method were compared with manual measurements with coefficient of determination R2 values of 0.89, 0.78, and 0.82, respectively. The results of the validation experiments denote that the cotton canopy volume obtained using the alpha shape algorithm is in good agreement with that obtained using manual measurements; thus, this algorithm is employed for volume calculations in this design. Models were constructed to relate UAV flight parameters, cotton canopy volume, and spraying parameters to the duty cycle of variable control signals, which enabled the UAV to adjust the flow rate in real time to match the cotton canopy structure. In addition, a polygonal prescription map decoding algorithm based on the cotton canopy morphology was introduced. This algorithm is used for real-time decoding of prescription maps and extraction of flow, latitude, and longitude information. Finally, field experiments were conducted, and the results indicate that the UAV variable-rate spray system designed in this study exhibits biological efficacy (effective application thresholds provided by the pesticide manufacturer) comparable to that of the constant-rate spray system at a lower application rate. Furthermore, it exhibited superior spray deposition efficiency and coverage compared with the constant-rate spray system. Notably, the use of the variable-rate spray system in cotton crops resulted in a 43.37% reduction in spray volume while maintaining high application quality, providing an opportunity for pesticide and labor cost savings.

A Building Point Cloud Extraction Algorithm in Complex Scenes

Buildings are significant components of digital cities, and their precise extraction is essential for the three-dimensional modeling of cities. However, it is difficult to accurately extract building features effectively in complex scenes, especially where trees and buildings are tightly adhered. This paper proposes a highly accurate building point cloud extraction method based solely on the geometric information of points in two stages. The coarsely extracted building point cloud in the first stage is iteratively refined with the help of mask polygons and the region growing algorithm in the second stage. To enhance accuracy, this paper combines the Alpha Shape algorithm with the neighborhood expansion method to generate mask polygons, which help fill in missing boundary points caused by the region growing algorithm. In addition, this paper performs mask extraction on the original points rather than non-ground points to solve the problem of incorrect identification of facade points near the ground using the cloth simulation filtering algorithm. The proposed method has shown excellent extraction accuracy on the Urban-LiDAR and Vaihingen datasets. Specifically, the proposed method outperforms the PointNet network by 20.73% in precision for roof extraction of the Vaihingen dataset and achieves comparable performance with the state-of-the-art HDL-JME-GGO network. Additionally, the proposed method demonstrated high accuracy in extracting building points, even in scenes where buildings were closely adjacent to trees.

Estimation of the Living Vegetation Volume (LVV) for Individual Urban Street Trees Based on Vehicle-Mounted LiDAR Data

The living vegetation volume (LVV) can accurately describe the spatial structure of greening trees and quantitatively represent the relationship between this greening and its environment. Because of the mostly line shape distribution and the complex species of street trees, as well as interference from artificial objects, current LVV survey methods are normally limited in their efficiency and accuracy. In this study, we propose an improved methodology based on vehicle-mounted LiDAR data to estimate the LVV of urban street trees. First, a point-cloud-based CSP (comparative shortest-path) algorithm was used to segment the individual tree point clouds, and an artificial objects and low shrubs identification algorithm was developed to extract the street trees. Second, a DBSCAN (density-based spatial clustering of applications with noise) algorithm was utilized to remove the branch point clouds, and a bottom-up slicing method combined with the random sampling consistency iterative method algorithm (RANSAC) was employed to calculate the diameters of the tree trunks and obtain the canopy by comparing the variation in trunk diameters in the vertical direction. Finally, an envelope was fitted to the canopy point cloud using the adaptive AlphaShape algorithm to calculate the LVVs and their ecological benefits (e.g., O2 production and CO2 absorption). The results show that the CSP algorithm had a relatively high overall accuracy in segmenting individual trees (overall accuracy = 95.8%). The accuracies of the tree height and DBH extraction based on vehicle-mounted LiDAR point clouds were 1.66~3.92% (rRMSE) and 4.23~15.37% (rRMSE), respectively. For the plots on Zijin Mountain, the LVV contribution by the maple poplar was the highest (1049.667 m3), followed by the sycamore tree species (557.907 m3), and privet’s was the lowest (16.681 m3).

Three-Dimensional Structure Measurement for Potted Plant Based on Millimeter-Wave Radar

Potted plant canopy extraction requires a fast, accurate, stable, and affordable detection system for precise pesticide application. In this study, we propose a new method for extracting three-dimensional canopy information of potted plants using millimeter-wave radar and evaluate the system on plants in static, rotating, and rotating-while-spraying states. The position and rotation speed of the rotating platform are used to compute the rotation–translation matrix between point clouds, enabling the multi-view point clouds to be overlaid on the world coordinate system. Point cloud extraction is performed by applying the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN), while an Alpha-shape algorithm is used for three-dimensional reconstruction of the canopy. Our measurement results for the 3D reconstruction of plants at different growth stages showed that the reconstruction model has higher accuracy under the rotation condition than that under the static condition, with average relative errors of 41.61% and 10.21%, respectively. The significant correlation between the sampling data with and without spray reached 0.03, indicating that the effect of the droplets on radar detection during the spray process can be neglected. This study provides guidance for plant canopy detection using millimeter-wave radar for advanced agricultural informatization and automation.

Fast Estimation of Loader’s Shovel Load Volume by 3D Reconstruction of Material Piles

Fast and accurate measurement of the volume of earthmoving materials is of great significance for the real-time evaluation of loader operation efficiency and the realization of autonomous operation. Existing methods for volume measurement, such as total station-based methods, cannot measure the volume in real time, while the bucket-based method also has the disadvantage of poor universality. In this study, a fast estimation method for a loader’s shovel load volume by 3D reconstruction of material piles is proposed. First, a dense stereo matching method (QORB–MAPM) was proposed by integrating the improved quadtree ORB algorithm (QORB) and the maximum a posteriori probability model (MAPM), which achieves fast matching of feature points and dense 3D reconstruction of material piles. Second, the 3D point cloud model of the material piles before and after shoveling was registered and segmented to obtain the 3D point cloud model of the shoveling area, and the Alpha-shape algorithm of Delaunay triangulation was used to estimate the volume of the 3D point cloud model. Finally, a shovel loading volume measurement experiment was conducted under loose-soil working conditions. The results show that the shovel loading volume estimation method (QORB–MAPM VE) proposed in this study has higher estimation accuracy and less calculation time in volume estimation and bucket fill factor estimation, and it has significant theoretical research and engineering application value.

Automated Determination of the Volume of Loose Engineering Deposits Using Terrestrial Laser Scanning

A new method for automatic volume determination of loose engineering deposits (LEDs) using point clouds collected by terrestrial laser scanning is proposed. The method starts with a problem of lacking bottom surface point clouds when scanning LEDs, assuming that the bottom surface is flat, a spatial plane is generated based on the plane fitting to the bottom surface. Then the sample point cloud is projected on this fitted plane to construct the subface point clouds, which, together with the initial point cloud data, are stitched into complete point cloud data with good closure, integrity, and accuracy. The 3D surface model established by introducing the alpha shape algorithm calculates the volume, and the validation experiments on three soil mound models and one field experiment on LEDs with a minimum average error of only 1.69% fully validate the effectiveness and accuracy of the method. The encouraging results show that effective volume calculation can be performed on realistic LEDs using the proposed method.

Interplay of shell evolution and oxidation depth on the ignition and combustion behavior of aluminum nanoparticles

Shell morphology and oxidation depth can significantly affect the combustion effectiveness of aluminum nanoparticles (ANPs). This study presents an approach to dynamically detect and reconstruct irregular ANP surfaces using the Alpha Shape algorithm. We also introduce a visualization technique to observe nanoscale density, per-atom stress, charge, and temperature distribution of ANPs. Our findings reveal a striking phenomenon where a transitory cavity forms in the particle center during ignition, playing a significant role in the overall ignition and combustion process of ANPs. The reaction rate is found to be closely associated with the formation and collapse of the cavity, with over half of the energy being released in this process. Furthermore, the study highlights the impact of oxidation depth on ANP combustion behavior. In cases of mild oxidation, a breach in the weak area of the shell leads to the leakage of aluminum atoms from the core and the eventual shell collapse. The duration of physiochemical stages during ignition and combustion varies with oxidation depth but generally conforms to established principles. This study is expected to offer valuable insight into the interplay between shell morphology and oxidation depth on the ignition and combustion behavior of ANPs for researchers and engineers in the field of material science and combustion technology.

A non-destructive approach to estimate buttress volume using 3D point cloud data

Buttressed trees provide mechanical support for themselves and offer essential ecological functions, such as nutrient acquisition, while being one of the largest sources of volume or biomass estimation variation in tropical forests. In this study, we collected 51 buttressed trees from (33) Democratic Republic of Congo, (12) Indonesia, and (6) Costa Rica, including (48) point clouds, and (3) destructive measurement. Specifically, we compared the performance of the Alpha Shape Algorithm (ASA) and the Slice Triangulation (ST) method on buttress volume estimation based on 30 point clouds with two species. Six point clouds from Costa Rica were used to validate the 3D surface reconstruction method. Meanwhile, we developed three allometric models based on 36 point clouds: a diameter above the buttress-based (DAB, 39 to 203 cm) model, a diameter computed from the non-convex area (Darea130) model, and the convex hull perimeter (Dconvex130) of the breast height model. The developed models were validated with independent data, including (6) point clouds and (3) destructive measurements, to highlight the broader contextualization and application of these methods. Volume estimated by the ASA and ST showed a high agreement with the reference volume acquired using the Smalian formula (relative RMSE of 0.07 and 0.11, respectively, regardless of species effect). ASA was also robust when modeling trees with more and shallower horizontal buttresses. Darea130 was the most accurate predictor to estimate buttress volume, with a lower Akaike information criterion (−66.25) than DAB (−59.55) and Dconvex130 (30.56); however, DAB and Darea130 (relative RMSE of 0.21 and 0.23, respectively) showed similar performance when validated with independent datasets. Our results indicate that the ASA approach performs better than both the ST and allometric models used in this study. Furthermore, the ASA method can help correct the bias in the present and past estimates of volume and biomass of large trees, which are foundational components for understanding biomass allocation and dynamics in tropical forests contemporary fields.

A particle finite element method based on Level–Set functions

Since the seminal work of Idelsohn, Oñate and Del-Pin (2004), the Particle Finite Element Method (PFEM) has relied on a Delaunay triangulation and the Alpha–Shape (AS) algorithm in the remeshing process. This approach guarantees a good quality of the Lagrangian mesh, but introduces a list of shortcomings that demand geometrical treatments tailored to each problem. In order to improve the remeshing process in PFEM, this work proposes the use of a Level–Set (LS) function instead of the Alpha–Shape algorithm. Since the Level–Set considers the boundary of the fluid and its interior, and not only a geometric criterion as does the Alpha–Shape, the proposed strategy (PFEM–LS) shows more robustness than the classical approach (PFEM–AS) owing to three main improvements. First, the LS function allows for a better control over the elements that are created during the fluid/fluid contact, which helps to reduce mass creation. Second, it helps to preserve the smoothness of the free surface and to reduce mass loss. Third, it allows the meshing of solitary particles that are detached from the free surface, which improves the representation of drops in PFEM. The methodology is presented and validated using free surface flow problems in 2D.

Calculation Method of Canopy Dynamic Meshing Division Volumes for Precision Pesticide Application in Orchards Based on LiDAR

The canopy volume of fruit trees is an important input for the precise and varying application of pesticides in orchards. The fixed mesh division method is mostly used to calculate canopy volumes with variable target-oriented spraying. To reduce the influence of the working speed on the detection accuracy under a fixed mesh width division, the cuboid accumulation of divided areas (CADAs), which is a light detection and ranging (LiDAR) online detection method for a fruit tree canopy volume based on dynamic mesh division, is proposed in this paper. In the method, the area is divided according to the number of unilateral nozzles of the sprayer in the canopy height direction of the fruit tree, and the mesh width is dynamically adjusted according to the change in the working speed in the moving direction of the sprayer. To verify the accuracy and applicability of the method, the simulation canopy and peach tree canopy detection experiments were carried out. The test results show that the CADA method can be used to calculate the contour and volume of the canopy. However, detection errors easily occur at the edge of the canopy, resulting in a detection error of 8.33% for the simulated canopy volume. The CADA method has a good detection accuracy under different moving speeds and fruit tree canopy sizes. At a speed of 1 m/s, the detection accuracy of the canopy volume reaches 99.18%. Compared with the existing canopy volume calculation methods based on the alpha-shape algorithm and canopy meshing-profile characterization (CMPC), the detection accuracy of the CADA method is 2.73% and 7.22% better, respectively. This method can not only reduce the influence of the moving speed on the detection accuracy of the canopy volume, but also improve the detection accuracy. Thus, this method can provide theoretical support for the research and development of target-oriented variable spraying control systems for orchards.

Automatic recognition of slide mass and inversion analysis of landslide based on discrete element method

The slip surface is an important structure for analyzing the landslide mechanism, and it will be deformed by the traction and shear of the slide mass. For the landslide accident without artificial monitoring, if the slip surface can be restored according to the slope morphology before and after the landslide, information such as the safety factor before the damage to the slope and the volume of the slide mass can be obtained by the position of slip surface, which helps engineers to analyze the cause of the landslide. In this paper, an automatic slide mass identification and landslide inversion analysis method based on discrete element simulation is proposed. The method combines Gaussian Mixture Model (GMM), alpha shape algorithm, discrete element simulation, and the morphology of the slope before and after the landslide to invert the slip surface of the slope. This method is suitable for two and three dimensional slope models. In addition, the effect of the slope angle on the deformation of the slip surface is analyzed.

Estimating plant area density of individual trees from discrete airborne laser scanning data using intensity information and path length distribution

Plant area density (PAD) of individual trees is an important structural indicator related to tree growth status, stress levels due to pests and diseases, photosynthesis potential, and evapotranspiration. Airborne laser scanning (ALS) provides unprecedented 3D information for mapping forest canopy parameters. Previous studies mainly focused on mapping stand-level and 2D leaf area index. This study proposes a method to estimate PAD from discrete and multiple return ALS data at individual tree scales. The proposed method uses path length distribution to eliminate crown-shape-induced clumping, as well as intensity information to estimate crown transmittance from relative low-density points. The path length distribution is derived from the 3D crown boundary contours created by an alpha shape algorithm, which explicitly considers the non-uniform LiDAR pulse penetration distances. Pulse intensity is calibrated with the nearest pure-ground pulse to mitigate the need for prior leaf and ground reflectance information, which can be used in areas with a heterogeneous background. The proposed method was evaluated both in virtual experiments as well as with terrestrial laser scanning (TLS) data. The virtual experiments used the large-scale remote sensing data and image simulation model (LESS) to simulate virtual ALS scanning data based on abstract and realistic canopies. Results showed that the ALS-derived PAD is highly accurate, with RMSE less than 0.02 and R2 > 0.99 for the abstract sphere and cube crowns, and RMSE = 0.19 and R2 = 0.578 for the realistic crowns. The comparison with TLS of a birch plot shows that the ALS-derived PAD is consistent with those derived from TLS, with RMSE = 0.14 and R2 = 0.46. This study demonstrated that using the full intensity and geometry information of a point cloud is capable of generating high-resolution forest parameters from ALS data.

Effects of laser scanner quality and tractor speed to characterise apple tree canopies

Precision orchard management requires non-destructive sensor technologies to identify key sources of variation in apple yield and quality so operations such as fruit thinning, root-pruning as well as harvesting can be better targeted and optimised. We compared the data quality of two light detection and ranging (LiDAR) scanners to assess their ability to estimate the volume of ‘Braeburn’ apple trees. Scanner one was a high-quality tractor-mounted LiDAR (SICK, LDMRS 400001, Düsseldorf, Germany) and the second system consisted of two lower-cost SICK LiDAR sensors fitted on a commercial orchard sprayer. To compare the two scanner systems, two tree row sections with different growth vigour were created by targeted root pruning to reduce annual shoot growth. LiDAR scans and reference assessments were made at the end of the growing season. In addition, LiDAR data were obtained at two tractor speeds: 1.5 and 7.0 km/h (standard spraying speed). Results show key canopy features like the beginning/end of rows or large gaps can be detected with the lower-cost sensors. However, tractor speed had a major effect on data quality. At 1.5 km/h, alpha-shaped tree volumes of the high-quality LiDAR show a high correlation (R2 = 0.84) with the reference assessments, whereas the data quality of the lower-cost sensors was not high enough to represent the tree volume with the alpha-shape algorithm (R2 = 0.09). Moreover, this work raises the question of how precise a sensor-based tree volume needs to be determined to describe heterogeneity in an orchard. Specifically, how to best match sensor data to manual methods of canopy measurement and allow sensor data to be used for multiple applications.

A calculation method of phenotypic traits based on three-dimensional reconstruction of tomato canopy

Accurate and rapid acquisition of tomato canopies’ phenotypic traits was significant for variety breeding, cultivation, and scientific management. Manual measurements were time-consuming, laborious, and error-prone. The large devices in the field were lack of mobility, while the single perspective was limited by the environment’s obstruction, making it challenging to achieve high-throughput detection of tomato plants’ phenotypes. Therefore, a method for high-throughput detection of tomato canopy’s phenotypic traits was proposed based on three-dimensional (3D) structure reconstruction with multi-views. First, the tomato variety named Dongnong 708 was set as an experimental object. The acquisition platform was constructed using three Kinect 2.0 sensors to acquire a full range point cloud of tomato canopy at crucial growth stages, including the initial flowering stage, florescence, and primary fruit stage. Second, the background and interference noises were removed by Conditional filtering and Statistical Outlier Removal (SOR) filtering. Then, its structural characteristic points were extracted, and spatial position was registered by combining with Intrinsic Shape Signatures (ISS) and Iterative Closest Point (ICP) algorithms. In addition, the 3D-Normal Distributions Transform (NDT) algorithm was used to realize accurate registration of three perspectives’ point clouds. Compared with NDT and ICP algorithms, the average error of the proposed methods here was 0.027, reduced by 0.02 and 0.04, respectively. Finally, the contour of tomato canopy was extracted using the AlphaShape algorithm. Based on the reconstructed result, plant height, canopy width, and leafstalk angle, were calculated. The results showed that the correlation coefficients were 0.9615, 0.809 and 0.9014 between the calculated values and measured values. The average errors were 1.38 cm, 5.1° and 1.92 cm, respectively. The methods proposed in the paper could be used as a rapid detection method for quantitative indexes of the phenotypic traits for the tomato canopy and provide technical support for breeding, scientific cultivation, and environmental regulation.

3D RECONSTRUCTION AND RENDERING VISUALIZATION OF TUNNEL POINT CLOUD

Abstract. This paper presents a method for rapid and automatic visualisation of over-undercutting based on 3D reconstruction and rendering of tunnel point clouds. The method uses model filtering and our improved voxel filtering for point cloud lite to maximise the preservation of the original tunnel point cloud features while streamlining the point cloud. The 3D reconstruction is then completed according to our proposed alpha-shape algorithm with low reconstruction parameters combined with a fast model optimisation model, which achieves high reconstruction accuracy while significantly improving computational efficiency to meet the requirements of the tunneling project. The rendering method is calculated by selecting the RGB values. The darker the colour, the heavier the degree of over-excavation or under-excavation. According to the calculated over-excavation or under-excavation value, the ratio of the point in the over-excavation or under-excavation interval is obtained in turn, and the gradient is rendered by increasing the base colour of each interval proportionally; the kd-tree is then introduced to calculate and visualise the colour information of the reconstructed model. In addition, this paper uses real-world tunnel point cloud data and demonstrates that the method meets expectations through qualitative and quantitative evaluation. Visualisation of the tunnel point cloud is accomplished while maintaining speed, visualising the over-under-excavation situation and providing an outlook for future work.

Power Pylon Reconstruction from Airborne LiDAR Data Based on Component Segmentation and Model Matching

In recent years, with the rapid growth of State Grid digitization, it has become necessary to perform three-dimensional (3D) reconstruction of power elements with high efficiency and precision to achieve full coverage when simulating important transmission lines. Limited by the performance of acquisition equipment and the environment, the actual scanned point cloud usually has problems such as noise interference and data loss, presenting a great challenge for 3D reconstruction. This study proposes a model-driven 3D reconstruction method based on Airborne LiDAR point cloud data. Firstly, power pylon redirection is realized based on the Principal Component Analysis (PCA) algorithm. Secondly, the vertical and horizontal distribution characteristics of the power pylon point cloud and the graphical characteristics of the overall two-dimensional (2D) orthographic projection are analyzed to determine segmentation positions and the key segmentation position of the power pylon. The 2D alpha shape algorithm is adopted to obtain the pylon body contour points, and then the pylon feature points are extracted and corrected. Based on feature points, the components of original pylon and model pylon are registered, and the distance between the original point cloud and the model point cloud is calculated at the same time. Finally, the model with the highest matching degree is regarded as the reconstructed model of the pylon. The main advantages of the proposed method include: (1) identifying the key segmentation position according to the graphical characteristics; (2) for some pylons with much missing data, the complete model can be accurately reconstructed. The average RMSE (Root-Mean-Square Error) of all power pylon components in this study was 15.4 cm. The experimental results reveal that the effects of power pylon structure segmentation and reconstruction are satisfactory, which provides method and model support for digital management and security analysis of transmission lines.

Drivable Dirt Road Region Identification Using Image and Point Cloud Semantic Segmentation Fusion

Driving scene understanding is an essential technique for realizing autonomous driving. Although many large-scale datasets for autonomous driving have enabled studies in terms of driving scene understanding, unpaved dirt roads have not been considered as their target driving environment. Drivable region identification is important in dirt roads to prevent damage to the vehicle and passengers. Semantic segmentation of camera images and lidar point clouds have been used to recognize the environment around an autonomous vehicle. The road and objects are recognized by classifying image pixels and point clouds into semantic classes. In this study, we introduce a perception method for drivable region identification on dirt roads through fusion of image and point cloud semantic segmentation. Our approach includes an image semantic segmentation algorithm and a point cloud semantic segmentation algorithm, which we combine into a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bird’s-eye-view grid map</i> format. To transform the point-wise drivable region identification results into the area-wise information, we adopt the alphashape algorithm. Speed improvements are made by using small proportion of drivable points, and the accuracy degradation is compensated by accumulating the perception results along the time.