Laser Scanning Point Cloud Data Research Articles

Efficient geological point cloud registration method combining dimension reduction and feature points

In geological scene registration with laser-scanned point cloud data, traditional algorithms often face reduced precision and efficiency due to extensive data volume and scope, which increase complexity and computational demands. This study introduces, to our knowledge, a novel registration method to address these limitations. Through dimension reduction that integrates height and curvature data, this approach converts point clouds into images, streamlining feature extraction. Log-variance enhancement mitigates information loss from dimensionality reduction, aiding in coarse registration. Further, incorporating weighted distances of feature points into the Iterative Closest Point (ICP) algorithm improves precision in point matching. Experiments indicate an average threefold increase in initial registration efficiency compared to traditional coarse registration algorithms, with improvements in accuracy. The optimized ICP algorithm achieves 50% and 15% accuracy improvements across various datasets, enhancing large-scale geological point cloud data registration.

Extraction of Moso Bamboo Parameters Based on the Combination of ALS and TLS Point Cloud Data.

Extracting moso bamboo parameters from single-source point cloud data has limitations. In this article, a new approach for extracting moso bamboo parameters using airborne laser scanning (ALS) and terrestrial laser scanning (TLS) point cloud data is proposed. Using the field-surveyed coordinates of plot corner points and the Iterative Closest Point (ICP) algorithm, the ALS and TLS point clouds were aligned. Considering the difference in point distribution of ALS, TLS, and the merged point cloud, individual bamboo plants were segmented from the ALS point cloud using the point cloud segmentation (PCS) algorithm, and individual bamboo plants were segmented from the TLS and the merged point cloud using the comparative shortest-path (CSP) method. The cylinder fitting method was used to estimate the diameter at breast height (DBH) of the segmented bamboo plants. The accuracy was calculated by comparing the bamboo parameter values extracted by the above methods with reference data in three sample plots. The comparison results showed that by using the merged data, the detection rate of moso bamboo plants could reach up to 97.30%; the R2 of the estimated bamboo height was increased to above 0.96, and the root mean square error (RMSE) decreased from 1.14 m at most to a range of 0.35-0.48 m, while the R2 of the DBH fit was increased to a range of 0.97-0.99, and the RMSE decreased from 0.004 m at most to a range of 0.001-0.003 m. The accuracy of moso bamboo parameter extraction was significantly improved by using the merged point cloud data.

Tree Diameter at Breast Height Extraction Based on Mobile Laser Scanning Point Cloud

The traditional measurement method (e.g., field survey) of tree diameter circumference often has high labor costs and is time-consuming. Mobile laser scanning (MLS) is a powerful tool for measuring forest diameter at breast height (DBH). However, the accuracy of point cloud registration seriously affects the results of DBH measurements. To address this issue, this paper proposes a new method for extracting tree DBH parameters; it achieves the purpose of efficient and accurate extraction of tree DBH by point cloud filtering, single-tree instance segmentation, and least squares circle fitting. Firstly, the point cloud data of the plantation forest samples were obtained by a self-constructed unmanned vehicle-mounted mobile laser scanning system, and the ground point cloud was removed using cloth simulation filtering (CSF). Secondly, fast Euclidean clustering (FEC) was employed to segment the single-tree instances, and the point cloud slices at breast height were extracted based on the point sets of single-tree instances, which were then fitted in two dimensions using the horizontally projected point cloud slices. Finally, a circle fitting algorithm based on intensity weighted least squares (IWLS) was proposed to solve the optimal circle model based on 2D point cloud slices, to minimize the impact of misaligned point clouds on DBH measures. The results showed that the mean absolute error (MAE) of the IWLS method was 2.41 cm, the root mean square error (RMSE) was 2.81 cm, and the relative accuracy was 89.77%. Compared with the random sample consensus (RANSAC) algorithm and ordinary least squares (OLS), the MAE was reduced by 36.45% and 9.14%, the RMSE was reduced by 40.90% and 12.26%, and the relative accuracy was improved by 8.99% and 1.63%, respectively. The R2 value of the fitted curve of the IWLS method was the closest to 1, with the highest goodness of fit and a significant linear correlation with the true value. The proposed intensity weighted least squares circle-fitting DBH extraction method can effectively improve the DBH extraction accuracy of mobile laser scanning point cloud data and reduce the influence of poorly aligned point clouds on DBH fitting.

Modeling instream temperature from solar insolation under varying timber harvesting intensities using RPAS laser scanning

Stream temperatures are influenced by the amount of solar insolation they receive. Increasing stream temperatures associated with climate warming pose detrimental health risks to freshwater ecosystems. In British Columbia (BC), Canada, timber harvesting along forested streams is managed using riparian buffer zones of varying widths and designations. Within buffer zones, depending on distance from the stream, selective thinning may be permitted or harvest may be forbidden. In this study, we used airborne laser scanning (ALS) point cloud data acquired via a remotely piloted aircraft system (RPAS) to derive forest canopy characteristics that were then used to estimate daily incoming summer and fall solar insolation for five stream reaches in coastal conifer-dominated temperate forests in Vancouver Island, BC, Canada. We then examined empirical relationships between estimated insolation and actual instream temperature measurements. Based on these empirical relationships, the potential effects of timber harvest on instream temperatures were simulated by comparing scenarios of different riparian forest harvest intensities. Our results indicated that modeled solar insolation explained 43–90 % of the variation in observed stream reach temperatures, and furthermore, when a single cold-water stream reach was excluded explained an overall 81 % of variation. Simulated harvesting scenarios generally projected increases in maximum stream reach temperatures 1–2 °C in summer and early fall months. However, in a full clearcut scenario (i.e. where all trees were removed), maximum stream reach temperatures increased as much as 5.8 °C. Our results emphasize the importance of retaining riparian vegetation for the maintenance of habitable temperatures for freshwater-reliant fish with thermal restrictions. In addition, we demonstrate the feasibility of RPAS-based monitoring of stream reach shading and canopy cover, enabling detailed assessment of environmental stressors faced by fish populations under climate warming.

Real-time laser scanning for conditioned coarse-grained soil monitoring on conveyor belt in earth pressure balance shield

To address the hysteresis in assessing the workability of conditioned soils and the inefficiency in estimating the soil volume flow rate in tunnelling practice, a novel real-time assessment and monitoring approach was proposed. Based on laser scanning technology and point cloud data analysis, the approach can monitor the workability and the volume flow rate of conditioned soils on the horizontal conveyor belt of an earth pressure balance (EPB) shield in real-time. Firstly, the accuracy of laser scanning measurements was verified by comparing manually measured slump and spread values with laser scanning results. Then, a series of model tests for scanning the coarse-grained soils with different conditioning parameters and belt speeds on the horizontal conveyor belt were performed. The appropriate ranges of secant angle and unevenness of soil cross-sectional profile were obtained for assessing the workability of conditioned soils. The volume flow rate was calculated by integrating the cross-sectional areas of conditioned soils. Finally, the proposed approach was successfully applied in identifying the workability of conditioned soil and its discharge rate in the EPB shield tunnelling project. This study contributes to the real-time monitoring of conditioned soils on the horizontal conveyor belt and provides practical insights for the optimal management of EPB shield tunnelling.

A Comprehensive Review of 3D Laser Scanning Point Cloud Data Processing Techniques: Assessing the Dominant Focus and Exploring 3D Modeling and Data Optimization

A Comprehensive Review of 3D Laser Scanning Point Cloud Data Processing Techniques: Assessing the Dominant Focus and Exploring 3D Modeling and Data Optimization

Dimension measurement method of ring forgings based on laser scanning and multiple regression filtering algorithm

In order to detect the radial is characterized and the isolated points that deviate from the target point cloud are removed using statistical section dimensions of ring parts in the forging process, we present a new method that processes laser scanning data of the outer radial section profile of ring forgings. Based on regression analysis and point cloud filtering, a multiple regression filtering algorithm is proposed to process the point cloud data for the measurement of the outer diameter of ring forgings. First, the laser scanned point cloud data filtering methods. The outlier clusters are then removed using regression analysis and processed by Gaussian regression filtering algorithms to obtain point cloud data with good circularity characteristics. Next, the Least Squares principle is used to fit a circle to the processed data to obtain information on the external diameter of the ring forgings to be measured. Finally, the feasibility of the method is verified by measuring the outer dimensions of the elliptical ring forgings in the experiment and performing error analysis.

Benchmark of multi-view Terrestrial Laser Scanning Point Cloud data registration algorithms

This study addresses multi-view Terrestrial Laser Scanning Point Cloud data registration methods. Multiple rigid point cloud data registration is mandatory for aligning all scans into a common reference frame and it is still considered a challenge looking from a large-scale surveys point of view. The goal of this work is to support the development of cutting-edge registration methods in geoscience and mobile robotics domains. This work evaluates 3 data sets of total 20 scenes available in the literature. This paper provides a novel open-source framework for multi-view Terrestrial Laser Scanning Point Cloud data registration benchmarks. The goal was to verify experimentally which registration variant can improve the open-source data looking from the quantitative and qualitative points of view. In particular, the following scanners provided measurement data: Z+F TLS Imager 5006i, Z+F TLS Imager 5010C, Leica ScanStation C5, Leica ScanStation C10, Leica P40 and Riegl VZ-400. The benchmark shows an impact of the metric e.g. point to point, point to projection onto a plane, plane to plane etc..., rotation matrix parameterization (Tait–Bryan, quaternion, Rodrigues) and other implementation variations (e.g. multi-view Normal Distributions Transform, Pose Graph SLAM approach) onto the multi-view data registration accuracy and performance. An open-source project is created and it can be used for improving existing data sets reported in the literature, it is the added value of the presented research. The combination of metrics, rotation matrix parameterization and optimization algorithms creates hundreds of possible approaches. It is shown that chosen metric is a dominant factor in data registration. The rotation parameterization and other degrees of freedom of proposed variants are rather negligible compared with chosen metric. Most of the proposed approaches improve registered reference data provided by other researchers. Only for 2 from 20 scenes it was not possible to provide significant improvement. The largest improvements are evident for large-scale scenes. The project is available and maintained at https://github.com/MapsHD/HDMapping.

Road potholes detection from MLS point clouds

The extraction of pavement damage information is one of the major difficulties in the application research of mobile laser scanning point cloud data. To address the problem of inaccurate detection results by using only relative distance to detect potholes, this paper proposes a novel pothole detection method that combines directed distance and skewed distribution. Firstly, the rapid positioning of the pothole is realized by the directed distance, which is calculated from the points and the local fitted plane. And monomerization and denoising of potential potholes are achieved by density clustering. Then, the new accurate plane is fitted by the surrounding pavement points of the potential pothole to obtain accurate directed distances. The negative skewed distribution of the directed distance histogram and the skewness coefficient are used for the accurate determination of the pothole. Finally, the three-dimensional geometric features of the pothole are extracted. Experiments were carried out on a road with poor road conditions. The experimental results validated the effectiveness and practicality of the proposed method. It can achieve automatic detection of potholes with different shapes and deformation degrees, and has effectively improved the efficiency of automatic road inspection.

Soft Segmentation of Terrestrial Laser Scanning Point Cloud of Forests

As the three-dimensional (3D) laser scanner is widely used for forest inventory, analyzing and processing point cloud data captured with a 3D laser scanner have become an important research topic in recent years. The extraction of single trees from point cloud data is essential for further investigation at the individual tree level, such as counting trees and trunk analysis, and many developments related to this topic have been published. However, constructing an accurate and automated method to obtain the tree crown silhouette from the point cloud data is challenging because the tree crowns often overlap between adjacent trees. A soft segmentation method that uses K-Nearest Neighbor (KNN) and contour shape constraints at the overlap region is proposed to solve this task. Experimental results show that the visual effect of the tree crown shape and the precision of point cloud segmentation have improved. It is concluded that the proposed method works well for tree crown segmentation and silhouette reconstruction from the terrestrial laser scanning point cloud data of the forest.

A new approach to detect surface defects from 3D point cloud data with surface normal Gabor filter (SNGF)

Surface defect detection is essential feedback for quality control in manufacturing processes. This paper presents a new defect detection method, Surface Normal Gabor Filter (SNGF), to detect surface defects using laser scanning point cloud data. The SNGF method transfers the 3D point cloud data to surface normal vectors to normalize the surface topology geometry. The surface normal vectors are then converted into complex numbers and processed by a Gabor Filter to extract defect-induced geometric features. The feasibility of SNGF is validated in the case of studies for detecting different types of defects on textured surfaces through simulations and experiments. The experimental results show improved stability in detecting defects with different sizes compared to the conventional Region Growing Segmentation Algorithm (RGSA). In addition, test results show the running time of SNGF methods is up to 138 times shorter than the RGSA when processing point cloud data sets with a range of 19,600∼313,600 data points.

Screening of Ginkgo Individuals with Superior Growth Structural Characteristics in Different Genetic Groups Using Terrestrial Laser Scanning (TLS) Data.

With the concept of sustainable management of plantations, individual trees with excellent characteristics in plantations have received attention from breeders. To improve and maintain long-term productivity, accurate and high-throughput access to phenotypic characteristics is essential when establishing breeding strategies. Meanwhile, genetic diversity is also an important issue that must be considered, especially for plantations without seed source information. This study was carried out in a ginkgo timber plantation. We used simple sequence repeat (SSR) markers for genetic background analysis and high-density terrestrial laser scanning for growth structural characteristic extraction, aiming to provide a possibility of applying remote sensing approaches for forest breeding. First, we analyzed the genetic diversity and population structure, and grouped individual trees according to the genetic distance. Then, the growth structural characteristics (height, diameter at breast height, crown width, crown area, crown volume, height to living crown, trunk volume, biomass of all components) were extracted. Finally, individual trees in each group were comprehensively evaluated and the best-performing ones were selected. Results illustrate that terrestrial laser scanning (TLS) point cloud data can provide nondestructive estimates of the growth structural characteristics at fine scale. From the ginkgo plantation containing high genetic diversity (average polymorphism information content index was 0.719) and high variation in growth structural characteristics (coefficient of variation ranged from 21.822% to 85.477%), 11 excellent individual trees with superior growth were determined. Our study guides the scientific management of plantations and also provides a potential for applying remote sensing technologies to accelerate forest breeding.

KCV-B: BOOTSTRAP WITH CROSS-VALIDATION FOR DEEP LEARNING MODEL DEVELOPMENT, ASSESSMENT AND SELECTION

Abstract. This study investigates the inability of two popular data splitting techniques: train/test split and k-fold cross-validation that are to create training and validation data sets, and to achieve sufficient generality for supervised deep learning (DL) methods. This failure is mainly caused by their limited ability of new data creation. In response, the bootstrap is a computer based statistical resampling method that has been used efficiently for estimating the distribution of a sample estimator and to assess a model without having knowledge about the population. This paper couples cross-validation and bootstrap to have their respective advantages in view of data generation strategy and to achieve better generalization of a DL model. This paper contributes by: (i) developing an algorithm for better selection of training and validation data sets, (ii) exploring the potential of bootstrap for drawing statistical inference on the necessary performance metrics (e.g., mean square error), and (iii) introducing a method that can assess and improve the efficiency of a DL model. The proposed method is applied for semantic segmentation and is demonstrated via a DL based classification algorithm, PointNet, through aerial laser scanning point cloud data.

Estimation of the Mean Trace Length of Discontinuities in an Underground Drift Using Laser Scanning Point Cloud Data

In the drifts of underground metal mines, the extraction of rock mass discontinuity characteristics from point cloud models generated with laser scanning has become the main approach. However, the exposure of discontinuities is restricted in drifts, and the size of discontinuities cannot be measured directly. Therefore, it is necessary to use a reasonable sampling tool to estimate the mean trace length of the discontinuities that are mapped in the point cloud model. In this paper, a method to estimate the mean trace length of discontinuities using a three-dimensional (3D) model of a drift (3DM) is proposed. Through the point cloud data of a drift obtained using 3D laser scanning, the information on discontinuities in the surrounding rock was extracted; then, the mean trace length was estimated using 3DEC to set sampling windows on the roof and sidewall in the 3DM. By analyzing the difference between the circular sampling window and the rectangular sampling window using simulated cases, the estimation results showed that the mean trace length obtained using circular measuring windows in the 3DM was closer to the true trace length. Finally, the method was used in a practical engineering case in Jianshan Iron Mine, Panzhihua, Sichuan, China.

Road Boundary, Curb and Surface Extraction from 3D Mobile LiDAR Point Clouds in Urban Environment

According to the spatial structure characteristics of road curbs and road surfaces, a robust method for automatic extraction of road boundaries, road curbs and road surfaces was proposed using mobile laser scanning (MLS) point cloud data. Firstly, ground filtering was performed to separate ground points and non-ground points according to the angle between the normal vector of the point cloud and the direction vector of the z-axis. Secondly, based on the vertical and linear features of the road curb, the MLS trajectory points were used to extract road curb and road boundary points. Then, Euclidean clustering and fitting were performed on the road boundary point segments. Adjacent clusters were merged, and sparse points were densified. In addition, based on the principle that road surfaces are within road boundaries, road surface points were obtained in scanning line order. Two MLS point clouds with different resolutions and road roughness were tested. Compared with the manually calibrated reference road curb, the extraction completenesses of the road curb from the two datasets were 95.66% and 96.45%, respectively, and the extraction correctnesses of the road curb were 96.34% and 99.10%, respectively, with both qualities over 92%. The algorithm can effectively extract straight or curved road boundaries and road curbs from the point cloud data containing vehicles, pedestrians and obstacle occlusions in an urban environment, and is applicable to MLS point cloud data with different resolutions and roughness.

Above-Ground Biomass Estimation of Plantation with Complex Forest Stand Structure Using Multiple Features from Airborne Laser Scanning Point Cloud Data

Accurate forest above-ground biomass (AGB) estimation is important for dynamic monitoring of forest resources and evaluation of forest carbon sequestration capacity. However, it is difficult to depict the forest’s vertical structure and its heterogeneity using optical remote sensing when estimating forest AGB, for the reason that electromagnetic waves cannot penetrate the canopy’s surface to obtain low vegetation information, especially in subtropical and tropical forests with complex layer structure and tree species composition. As an active remote sensing technology, an airborne laser scanner (ALS) can penetrate the canopy surface to obtain three-dimensional structure information related to AGB. This paper takes the Jiepai sub-forest farm and the Gaofeng state-owned forest farm in southern China as the experimental area and explores the optimal features from the ALS point cloud data and AGB inversion model in the subtropical forest with complex tree species composition and structure. Firstly, considering tree canopy structure, terrain features, point cloud structure and density features, 63 point cloud features were extracted. In view of the biomass distribution differences of different tree species, the random forest (RF) method was used to select the optimal features of each tree species. Secondly, four modeling methods were used to establish the AGB estimation models of each tree species and verify their accuracy. The results showed that the features related to tree height had a great impact on forest AGB. The top features of Cunninghamia Lanceolata (Chinese fir) and Eucalyptus are all related to height, Pinus (pine tree) is also related to terrain features and other broadleaved trees are also related to point cloud density features. The accuracy of the stepwise regression model is best with the AGB estimation accuracy of 0.19, 0.76, 0.71 and 0.40, respectively, for the Chinese fir, pine tree, eucalyptus and other broadleaved trees. In conclusion, the proposed linear regression AGB estimation model of each tree species combining different features derived from ALS point cloud data has high applicability, which can provide effective support for more accurate forest AGB and carbon stock inventory and monitoring.

Supervoxel-based extraction and classification of pole-like objects from MLS point cloud data

The three-dimensional (3D) instantiation expression of objects in road environments has significant applications in urban management and high-precision mapping. Pole-like objects, as the important part of road objects, their automatic and individual extraction and classification can reduce the cost of mapping and improve work efficiency. Therefore, this paper proposes a supervoxel-based method to automatically extract and classify individual pole-like objects from mobile laser scanning (MLS) point cloud data. First, supervoxels are generated through over-segmentation, and the vertical pole part of the pole-like objects are extracted via supervoxels region growing. Second, an uphill clustering method is used to individually segment the potential attachments on the vertical poles (the part of the pole-like objects except the vertical poles). Third, according to the spatial correspondence between the vertical poles and their attachments, the attachments are selected from the potential and matched with their corresponding vertical poles. Finally, the extracted individual pole-like objects are classified into four categories (street lights, cantilever traffic poles, street trees and others) according to their geometric characteristics. The proposed method was evaluated using two different MLS point cloud datasets. The experimental results demonstrate that the proposed method can efficiently extracted the pole-like objects from the two datasets, with the extraction rate of 92.4% and 98.6% respectively. Moreover, the proposed method can effectively classify the extracted pole-like objects.

Ribbon Road Surface Filtering in Vehicle-Borne Laser Scanning Point Cloud Data

Terrain data of road is the most commonly used basic data by the traffic department. The high-precision three-dimensional road data can restore the terrain features under the real scene, which has a wider application range. In order to obtain high-precision three-dimensional point cloud road data, taking the vehicle-borne laser scanning data as the research object, a method of ground filtering for ribbon road is proposed. First, the driving trajectory information of the road data collected by the vehicle is read. Then, according to the road characteristics, characteristics of the vehicle driving trajectory is used to control processing direction, and a grid index of the point cloud data is created. Second, feature descriptors based on the spatial distribution characteristics of road point cloud data are established, and rough road point cloud data is obtained. Finally, the profile data of the vehicle-borne laser point cloud in the vertical trajectory driving direction is extracted, and the accurate ribbon road and the micro-terrain data on both sides are obtained by the horizontal constraint line detection algorithm. The experiment and analysis are carried out by using two different ribbon road data. Experimental results show that the method can be applied to different road scenes.

High-Precision Method for Estimating the Three-Dimensional Green Quantity of an Urban Forest

Forests are an important component of urban ecosystems that offer a variety of ecological services; however, there are few reports on the application of high-precision three-dimensional (3D) laser-scanned point cloud data to quantify the 3D green volume. To quantitatively study the 3D green quantity of urban forests in Xi'an, China, aerial remote sensing and high-precision 3D laser-scanned point cloud data were used to build an estimation model using a differential method. The results show that the 3D green quantity model has a high estimation accuracy of 88.07%, with an R value of 0.89, and can be used as an inverse model for estimating the 3D standing wood volume. The method can be automated for data processing and has a high measurement efficiency, making it applicable to real situations and significantly improving the measurement accuracy of single-tree 3D green quantification. The total 3D green quantity in the main urban area of Xi’an is 37,475,294.7 m3, where areas containing the greatest green volume follow the order scenic sites > cultural and educational areas > traffic routes > residential areas > industrial areas. This method can be used to improve urban design, policy making, and healthy urban forests.

A Massive Laser Point Cloud Data Organization Strategy Based on the Mixed Model

In the process of data interaction, neighborhood query, filtering, visualization, and dynamic update of LiDAR point cloud data, how to efficiently organize and process massive point cloud data, and quickly index and locate any point in the point cloud and its neighborhood Search is a key issue to be solved urgently. In this paper, combining the advantages of a virtual grid with no interpolation loss on original data, sT spatial relationship, and low memory occupation of the octree, we design an index method based on the combination of virtual grid and adaptive octree based on dynamic scheduling of internal and external memory. Realize the organization and scheduling of massive LiDAR laser scanning point cloud data.