scholarly journals Automated Three-Dimensional Linear Elements Extraction from Mobile LiDAR Point Clouds in Railway Environments

2019 ◽  
Vol 4 (3) ◽  
pp. 46
Author(s):  
Luis Gézero ◽  
Carlos Antunes
Keyword(s):  
Three Dimensional ◽  
Simple Algorithm ◽  
Point Clouds ◽  
Lidar Data ◽  
Quality Information ◽  
Geometric Methods ◽  
Restricted Number ◽  
System Trajectory ◽  
Linear Elements ◽  
Cloud Points

The railway structures need constant monitoring and maintenance to ensure the train circulation safety. Quality information concerning the infrastructure geometry, namely the three-dimensional linear elements, are crucial for that processes. Along with this work, a method to automated extract three-dimensional linear elements from point clouds collected by terrestrial mobile LiDAR systems along railways is presented. The proposed method takes advantage of the stored cloud point’s attributes as an alternative to complex geometric methods applied over the point’s cloud coordinates. Based on the assumption that the linear elements to extract are roughly parallel to the rail tracks and therefore to the system trajectory, the stored scan angle value was used to restrict the number of cloud points that represents the linear elements. A simple algorithm is then applied to that restricted number of points to get the three-dimensional polylines geometry. The obtained values of completeness, correctness and quality, validate the use of the methodology for linear elements extraction from mobile LiDAR data gathered along railway environments.

scholarly journals MULTI-SCALE BASED EXTRACION OF VEGETATION FROM TERRESTRIAL LiDAR DATA FOR ASSESSING LOCAL LANDSCAPE

2015 ◽  
Vol II-3/W4 ◽  
pp. 263-270 ◽  
Author(s):  
T. Wakita ◽  
J. Susaki
Keyword(s):  
Urban Areas ◽  
Three Dimensional ◽  
Point Clouds ◽  
Lidar Data ◽  
Data Sets ◽  
Landscape Index ◽  
Terrestrial Lidar ◽  
Multi Scale ◽  
3D Point Clouds ◽  
Set Of Points

In this study, we propose a method to accurately extract vegetation from terrestrial three-dimensional (3D) point clouds for estimating landscape index in urban areas. Extraction of vegetation in urban areas is challenging because the light returned by vegetation does not show as clear patterns as man-made objects and because urban areas may have various objects to discriminate vegetation from. The proposed method takes a multi-scale voxel approach to effectively extract different types of vegetation in complex urban areas. With two different voxel sizes, a process is repeated that calculates the eigenvalues of the planar surface using a set of points, classifies voxels using the approximate curvature of the voxel of interest derived from the eigenvalues, and examines the connectivity of the valid voxels. We applied the proposed method to two data sets measured in a residential area in Kyoto, Japan. The validation results were acceptable, with F-measures of approximately 95% and 92%. It was also demonstrated that several types of vegetation were successfully extracted by the proposed method whereas the occluded vegetation were omitted. We conclude that the proposed method is suitable for extracting vegetation in urban areas from terrestrial light detection and ranging (LiDAR) data. In future, the proposed method will be applied to mobile LiDAR data and the performance of the method against lower density of point clouds will be examined.

scholarly journals Spherically Stratified Point Projection: Feature Image Generation for Object Classification Using 3D LiDAR Data

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7860
Author(s):  
Chulhee Bae ◽  
Yu-Cheol Lee ◽  
Wonpil Yu ◽  
Sejin Lee
Keyword(s):  
Three Dimensional ◽  
Object Classification ◽  
Point Clouds ◽  
Lidar Data ◽  
Image Generation ◽  
Channel Integration ◽  
Occupancy Probability ◽  
Evidence Collection ◽  
Data Points ◽  
Point Projection

Three-dimensional point clouds have been utilized and studied for the classification of objects at the environmental level. While most existing studies, such as those in the field of computer vision, have detected object type from the perspective of sensors, this study developed a specialized strategy for object classification using LiDAR data points on the surface of the object. We propose a method for generating a spherically stratified point projection (sP2) feature image that can be applied to existing image-classification networks by performing pointwise classification based on a 3D point cloud using only LiDAR sensors data. The sP2’s main engine performs image generation through spherical stratification, evidence collection, and channel integration. Spherical stratification categorizes neighboring points into three layers according to distance ranges. Evidence collection calculates the occupancy probability based on Bayes’ rule to project 3D points onto a two-dimensional surface corresponding to each stratified layer. Channel integration generates sP2 RGB images with three evidence values representing short, medium, and long distances. Finally, the sP2 images are used as a trainable source for classifying the points into predefined semantic labels. Experimental results indicated the effectiveness of the proposed sP2 in classifying feature images generated using the LeNet architecture.

scholarly journals An Automated Hierarchical Approach for Three-Dimensional Segmentation of Single Trees Using UAV LiDAR Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1999 ◽  
Author(s):  
Wanqian Yan ◽  
Haiyan Guan ◽  
Lin Cao ◽  
Yongtao Yu ◽  
Sha Gao ◽  
...  
Keyword(s):  
Mean Shift ◽  
Three Dimensional ◽  
Terrestrial Ecosystems ◽  
Point Clouds ◽  
Lidar Data ◽  
Mean Shift Algorithm ◽  
Robust Segmentation ◽  
Forest Ecosystem Services ◽  
Segmentation Approach ◽  
Aerial Vehicle

Forests play a key role in terrestrial ecosystems, and the variables extracted from single trees can be used in various fields and applications for evaluating forest production and assessing forest ecosystem services. In this study, we developed an automated hierarchical single-tree segmentation approach based on the high density three-dimensional (3D) Unmanned Aerial Vehicle (UAV) point clouds. First, this approach obtains normalized non-ground UAV points in data preprocessing; then, a voxel-based mean shift algorithm is used to roughly classify the non-ground UAV points into well-detected and under-segmentation clusters. Moreover, potential tree apices for each under-segmentation cluster are obtained with regard to profile shape curves and finally input to the normalized cut segmentation (NCut) algorithm to segment iteratively the under-segmentation cluster into single trees. We evaluated the proposed method using datasets acquired by a Velodyne 16E LiDAR system mounted on a multi-rotor UAV. The results showed that the proposed method achieves the average correctness, completeness, and overall accuracy of 0.90, 0.88, and 0.89, respectively, in delineating single trees. Comparative analysis demonstrated that our method provided a promising solution to reliable and robust segmentation of single trees from UAV LiDAR data with high point cloud density.

scholarly journals Roof Plane Segmentation from Airborne LiDAR Data Using Hierarchical Clustering and Boundary Relabeling

2020 ◽  
Vol 12 (9) ◽  
pp. 1363 ◽  
Author(s):  
Li Li ◽  
Jian Yao ◽  
Jingmin Tu ◽  
Xinyi Liu ◽  
Yinxuan Li ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Three Dimensional ◽  
Region Growing ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Building Models ◽  
Novel Approach ◽  
Airborne Lidar Data ◽  
Seed Points

The roof plane segmentation is one of the key issues for constructing accurate three-dimensional building models from airborne light detection and ranging (LiDAR) data. Region growing is one of the most widely used methods to detect roof planes. It first selects one point or region as a seed, and then iteratively expands to neighboring points. However, region growing has two problems. The first problem is that it is hard to select the robust seed points. The other problem is that it is difficult to detect the accurate boundaries between two roof planes. In this paper, to solve these two problems, we propose a novel approach to segment the roof planes from airborne LiDAR point clouds using hierarchical clustering and boundary relabeling. For the first problem, we first extract the initial set of robust planar patches via an octree-based method, and then apply the hierarchical clustering method to iteratively merge the adjacent planar patches belonging to the same plane until the merging cost exceeds a predefined threshold. These merged planar patches are regarded as the robust seed patches for the next region growing. The coarse roof planes are generated by adding the non-planar points into the seed patches in sequence using region growing. However, the boundaries of coarse roof planes may be inaccurate. To solve this problem, namely, the second problem, we refine the boundaries between adjacent coarse planes by relabeling the boundary points. At last, we can effectively extract high-quality roof planes with smooth and accurate boundaries from airborne LiDAR data. We conducted our experiments on two datasets captured from Vaihingen and Wuhan using Leica ALS50 and Trimble Harrier 68i, respectively. The experimental results show that our proposed approach outperforms several representative approaches in both visual quality and quantitative metrics.

scholarly journals Moving to Automated Tree Inventory: Comparison of UAS-Derived Lidar and Photogrammetric Data with Manual Ground Estimates

2020 ◽  
Vol 13 (1) ◽  
pp. 72
Author(s):  
Luiz Felipe Ramalho de Oliveira ◽  
H. Andrew Lassiter ◽  
Ben Wilkinson ◽  
Travis Whitley ◽  
Peter Ifju ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Three Dimensional ◽  
Point Clouds ◽  
Detection Algorithm ◽  
Unmanned Aircraft ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Individual Tree ◽  
Tree Detection

Unmanned aircraft systems (UAS) have advanced rapidly enabling low-cost capture of high-resolution images with cameras, from which three-dimensional photogrammetric point clouds can be derived. More recently UAS equipped with laser scanners, or lidar, have been employed to create similar 3D datasets. While airborne lidar (originally from conventional aircraft) has been used effectively in forest systems for many years, the ability to obtain important tree features such as height, diameter at breast height, and crown dimensions is now becoming feasible for individual trees at reasonable costs thanks to UAS lidar. Getting to individual tree resolution is crucial for detailed phenotyping and genetic analyses. This study evaluates the quality of three three-dimensional datasets from three sensors—two cameras of different quality and one lidar sensor—collected over a managed, closed-canopy pine stand with different planting densities. For reference, a ground-based timber cruise of the same pine stand is also collected. This study then conducted three straightforward experiments to determine the quality of the three sensors’ datasets for use in automated forest inventory: manual mensuration of the point clouds to (1) detect trees and (2) measure tree heights, and (3) automated individual tree detection. The results demonstrate that, while both photogrammetric and lidar data are well-suited for single-tree forest inventory, the photogrammetric data from the higher-quality camera is sufficient for individual tree detection and height determination, but that lidar data is best. The automated tree detection algorithm used in the study performed well with the lidar data, detecting 98% of the 2199 trees in the pine stand, but fell short of manual mensuration within the lidar point cloud, where 100% of the trees were detected. The manually-mensurated heights in the lidar dataset correlated with field measurements at r = 0.95 with a bias of −0.25 m, where the photogrammetric datasets were again less accurate and precise.

scholarly journals A VOXEL-BASED FILTERING ALGORITHM FOR MOBILE LIDAR DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 1433-1438 ◽  
Author(s):  
H. Qin ◽  
G. Guan ◽  
Y. Yu ◽  
L. Zhong
Keyword(s):  
Three Dimensional ◽  
Block Size ◽  
Point Clouds ◽  
Multiple Point ◽  
Lidar Data ◽  
Filtering Algorithm ◽  
Promising Solution ◽  
Overall Performance ◽  
Partition Structure ◽  
Global And Local

This paper presents a stepwise voxel-based filtering algorithm for mobile LiDAR data. In the first step, to improve computational efficiency, mobile LiDAR points, in xy-plane, are first partitioned into a set of two-dimensional (2-D) blocks with a given block size, in each of which all laser points are further organized into an octree partition structure with a set of three-dimensional (3-D) voxels. Then, a voxel-based upward growing processing is performed to roughly separate terrain from non-terrain points with global and local terrain thresholds. In the second step, the extracted terrain points are refined by computing voxel curvatures. This voxel-based filtering algorithm is comprehensively discussed in the analyses of parameter sensitivity and overall performance. An experimental study performed on multiple point cloud samples, collected by different commercial mobile LiDAR systems, showed that the proposed algorithm provides a promising solution to terrain point extraction from mobile point clouds.

scholarly journals ПОРІВНЯЛЬНИЙ АНАЛІЗ МЕТОДІВ ПОШУКУ НАЙБЛИЖЧИХ ТОЧОК НА ЗОБРАЖЕННЯХ ОБ’ЄКТІВ ТЕХНІЧНИХ СИСТЕМ

2021 ◽  
pp. 123-130
Author(s):  
М. Д. Мирненко ◽  
Д. М. Крицький ◽  
О. К. Погудіна ◽  
О. С. Крицька
Keyword(s):  
Three Dimensional ◽  
Point Clouds ◽  
Mutual Orientation ◽  
Icp Algorithm ◽  
Three Dimensional Objects ◽  
Key Points ◽  
Template Library ◽  
Cloud Points ◽  
Technical Systems ◽  
Nearest Points

The subject of the study is the process of mapping the construction of point clouds of technical systems using the algorithm of the nearest points. The goal is to minimize the alignment criterion by converting a set of cloud points Y into a set of cloud points X in a software product that uses an iterative closest point (ICP) algorithm. Objectives: to analyze the properties of input images that contain point clouds; to review the algorithms for identifying and comparing key points; implement a cloud comparison algorithm using the ISR algorithm; consider an example of the algorithm for estimating the approximate values of the elements of mutual orientation; implement software that allows you to compare files that contain point clouds and draw conclusions about the possibility of comparing them. The methods used are: search for points using the algorithm of iterative nearest points, the algorithm for estimating the approximate values of the elements of mutual orientation, the method of algorithm theory for the analysis of file structures STL (standard template library - format template library) format. The following results were obtained. The choice of the ICP algorithm for the task of reconstruction of the object of technical systems is substantiated; the main features of the ISR algorithm are considered; the algorithm of comparison of key points, and also optimization that allows reducing criterion of combination at the reconstruction of three-dimensional objects of technical systems results. Conclusions. The study found that the iterative near-point algorithm is more detailed and accurate when modeling objects. At the same time, this method requires very accurate values and when calculating the degree of proximity, the complexity of calculation by this algorithm increases many times. Whereas the algorithm for estimating the approximate values of the elements of mutual orientation does not require information about the approximate orientation of the point clouds, which simplifies the work and reduces the simulation time. It was found that not all files are comparable. Therefore, the software is implemented, which gives an opinion on the possibility of comparing points in the proposed two files, which contain clouds of points in the structure of the STL format.

scholarly journals Collision Avoidance of Hexacopter UAV Based on LiDAR Data in Dynamic Environment

2020 ◽  
Vol 12 (6) ◽  
pp. 975 ◽  
Author(s):  
Jongho Park ◽  
Namhoon Cho
Keyword(s):  
Collision Avoidance ◽  
Collision Detection ◽  
Three Dimensional ◽  
Dynamic Environment ◽  
Point Clouds ◽  
Lidar Data ◽  
Lidar System ◽  
State Estimate ◽  
Aerial Vehicle ◽  
Moving Obstacle

A reactive three-dimensional maneuver strategy for a multirotor Unmanned Aerial Vehicle (UAV) is proposed based on the collision cone approach to avoid potential collision with a single moving obstacle detected by an onboard sensor. A Light Detection And Ranging (LiDAR) system is assumed to be mounted on a hexacopter to obtain the obstacle information from the collected point clouds. The collision cone approach is enhanced to appropriately deal with the moving obstacle with the help of a Kalman filter. The filter estimates the position, velocity, and acceleration of the obstacle by using the LiDAR data as the associated measurement. The obstacle state estimate is utilized to predict the future trajectories of the moving obstacle. The collision detection and obstacle avoidance maneuver decisions are made considering the predicted trajectory of the obstacle. Numerical simulations, including a Monte Carlo campaign, are conducted to verify the performance of the proposed collision avoidance algorithm.

scholarly journals Automatic and Visual Processing Method of Non-Contact Monitoring for Circular Stormwater Sewage Tunnels Based on LiDAR Data

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1599 ◽  
Author(s):  
Xiongyao Xie ◽  
Mingrui Zhao ◽  
Jiamin He ◽  
Biao Zhou
Keyword(s):  
Visual Processing ◽  
Point Cloud ◽  
Single Point ◽  
Three Dimensional ◽  
Point Clouds ◽  
Processing Method ◽  
Lidar Data ◽  
Health State ◽  
Short Period ◽  
Point Data

The application of Light Detection And Ranging (LiDAR) technology has become increasingly extensive in tunnel structure monitoring. The proposed processing method aims to carry out non-contact monitoring for circular stormwater sewage tunnels and provides an efficient workflow. This allows the automatic processing of raw point data and the acquisition of visualization results to analyze the health state of a tunnel within a short period of time. The proposed processing method employs a series of algorithms to extract the point cloud of a single tunnel segment without obvious noise by main three steps: axis acquisition, segment extraction, and denoising. The tunnel axis is extracted by fitting boundaries of the tunnel point cloud projection in the plane. With the guidance of the axis, the entire preprocessed tunnel point cloud is segmented by equal division to get a section of the tunnel point cloud which corresponds to a single tunnel segment. Then, the noise in every single point cloud segment is removed by clustering the algorithm twice, based on the distance and intensity. Finally, clean point clouds of tunnel segments are processed by an effective deformation extraction processor to determine the ovality and to get a three-dimensional visual deformation nephogram. The proposed method can significantly improve the efficiency of LiDAR data processing and extend the application of LiDAR technology in circular stormwater sewage tunnel monitoring.

Extraction of Road Lane Markings from Mobile LiDAR Data

2021 ◽  
pp. 036119812098194
Author(s):  
Mustafa Zeybek
Keyword(s):  
Traffic Management ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Lidar Data ◽  
Safe Driving ◽  
Cloud Data ◽  
Detection Techniques ◽  
On The Road ◽  
The Right

This study presents a method for automatic extraction of road lane markings from mobile light detection and ranging (LiDAR) data. Road lanes and traffic signs on the road surface provide safe driving for drivers and aid traffic flow movement along the highway and street. Mobile LiDAR systems acquire massive datasets very quickly in a short time. To simplify the data structure and feature extraction, it is essential for traffic management personnel to apply the right methods. Road lanes must be visible and are a major factor in road safety for drivers. In this study, a methodology is devised and implemented for the extraction of features such as dashed lines, continuous lanes, and direction arrows on the pavement from point clouds. Point cloud data was collected from the Riegl VMX-450 mobile LiDAR system. The alpha shape algorithm is implemented on a point cloud and compared with the widespread use of edge detection techniques applied for intensity-based raster images. The proposed methodology directly extracts three-dimensional and two-dimensional road features to control the quality of road markings and spatial positions with the obtained marking boundaries. State-of-the-art results are obtained and compared with manually digitized reference markings. The standard deviations were evaluated and acquired for intensity image-based and direct point cloud-based extractions, at 1.2 cm and 1.7 cm, respectively.

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