scholarly journals An Efficient and Accurate Method for Different Configurations Railway Extraction Based on Mobile Laser Scanning

2019 ◽  
Vol 11 (24) ◽  
pp. 2929 ◽  
Author(s):  
Rong Zou ◽  
Xiaoyun Fan ◽  
Chuang Qian ◽  
Wenfang Ye ◽  
Peng Zhao ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Accurate Method ◽  
Average Density ◽  
Railway Track ◽  
Track Geometry ◽  
Fitting Algorithm ◽  
Railway Maintenance ◽  
Night Period ◽  
Reflection Intensity

The precision of railway map is becoming a significant issue for autonomous train scheduling, monitoring and maintenance, related location-based service (LBS), and further ensuring travel safety. Mobile 3D laser scanning is an efficient method for making relative high-precision railway track maps, particularly during the night period of railway maintenance, for light detection and ranging (LiDAR) can work without ambient light. In this paper, we propose an efficient and accurate railway track vectorization method based on the LiDAR point clouds from the self-built train Mobile Laser Scanning (MLS) system. Our method takes full use of railway track geometry and reflection intensity feature of LiDAR, without any trajectory prior information. Firstly, clear track points are filtered by intensity; then, a K-means clustering fused Region-Grow Fitting algorithm is applied. It can not only extract the line vector of railway track, but also can tell the track branches apart, especially on bends and turnout. Experiments were carried on using point clouds with an average density of 490 points per square meter. The experimental results show that the method not only can quickly extract linear objects such as railway track and catenary, but also can detect the railways even in complex real-world topologies such as at bends and turnouts. The precision of the detection area in bends and turnouts are 90.32% and 81.31% respectively, the sensitivity is 83.27% and 83.33%, respectively. Moreover, it can identify the track networks.

scholarly journals ALGORITHM FOR THE AUTOMATIC ESTIMATION OF AGRICULTURAL TREE GEOMETRIC PARAMETERS USING AIRBORNE LASER SCANNING DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 629-632 ◽  
Author(s):  
E. Hadaś ◽  
A. Borkowski ◽  
J. Estornell
Keyword(s):  
Laser Scanning ◽  
Tree Height ◽  
Field Measurements ◽  
Point Clouds ◽  
Average Density ◽  
Geometric Parameters ◽  
Airborne Laser Scanning ◽  
Maximum Height ◽  
Airborne Laser ◽  
Crown Base Height

The estimation of dendrometric parameters has become an important issue for the agricultural planning and management. Since the classical field measurements are time consuming and inefficient, Airborne Laser Scanning (ALS) data can be used for this purpose. Point clouds acquired for orchard areas allow to determine orchard structures and geometric parameters of individual trees. In this research we propose an automatic method that allows to determine geometric parameters of individual olive trees using ALS data. The method is based on the α-shape algorithm applied for normalized point clouds. The algorithm returns polygons representing crown shapes. For points located inside each polygon, we select the maximum height and the minimum height and then we estimate the tree height and the crown base height. We use the first two components of the Principal Component Analysis (PCA) as the estimators for crown diameters. The α-shape algorithm requires to define the radius parameter <i>R</i>. In this study we investigated how sensitive are the results to the radius size, by comparing the results obtained with various settings of the R with reference values of estimated parameters from field measurements. Our study area was the olive orchard located in the Castellon Province, Spain. We used a set of ALS data with an average density of 4 points&thinsp;m<sip>&minus;2</sup>. We noticed, that there was a narrow range of the <i>R</i> parameter, from 0.48&thinsp;m to 0.80&thinsp;m, for which all trees were detected and for which we obtained a high correlation coefficient (>&thinsp;0.9) between estimated and measured values. We compared our estimates with field measurements. The RMSE of differences was 0.8&thinsp;m for the tree height, 0.5&thinsp;m for the crown base height, 0.6&thinsp;m and 0.4&thinsp;m for the longest and shorter crown diameter, respectively. The accuracy obtained with the method is thus sufficient for agricultural applications.

scholarly journals Extracting Rail Track Geometry from Static Terrestrial Laser Scans for Monitoring Purposes

2014 ◽  
Vol XL-5 ◽  
pp. 553-557 ◽  
Author(s):  
A. Soni ◽  
S Robson ◽  
B Gleeson
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Railway Track ◽  
Monitoring Tool ◽  
Track Geometry ◽  
Rail Track ◽  
Target Locations ◽  
Track Monitoring

This paper presents the capabilities of detecting relevant geometry of railway track for monitoring purposes from static terrestrial laser scanning (TLS) systems at platform level. The quality of the scans from a phased based scanner (Scanner A) and a hybrid timeof- flight scanner (Scanner B) are compared by fitting different sections of the track profile to its matching standardised rail model. The various sections of track investigated are able to fit to the model with an RMS of less than 3 mm. Both scanners show that once obvious noise and artefacts have been removed from the data, the most confident fit of the point cloud to the model is the section closest to the scanner position. The results of the fit highlight the potential to use this method as a bespoke track monitoring tool during major redevelopment projects where traditional methods, such as robotic total stations, results in missed information, for example due to passing trains or knocked prisms and must account for offset target locations to compute track parameters.

scholarly journals ALGORITHM FOR THE AUTOMATIC ESTIMATION OF AGRICULTURAL TREE GEOMETRIC PARAMETERS USING AIRBORNE LASER SCANNING DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 629-632
Author(s):  
E. Hadaś ◽  
A. Borkowski ◽  
J. Estornell
Keyword(s):  
Laser Scanning ◽  
Tree Height ◽  
Field Measurements ◽  
Point Clouds ◽  
Average Density ◽  
Geometric Parameters ◽  
Airborne Laser Scanning ◽  
Maximum Height ◽  
Airborne Laser ◽  
Crown Base Height

The estimation of dendrometric parameters has become an important issue for the agricultural planning and management. Since the classical field measurements are time consuming and inefficient, Airborne Laser Scanning (ALS) data can be used for this purpose. Point clouds acquired for orchard areas allow to determine orchard structures and geometric parameters of individual trees. In this research we propose an automatic method that allows to determine geometric parameters of individual olive trees using ALS data. The method is based on the α-shape algorithm applied for normalized point clouds. The algorithm returns polygons representing crown shapes. For points located inside each polygon, we select the maximum height and the minimum height and then we estimate the tree height and the crown base height. We use the first two components of the Principal Component Analysis (PCA) as the estimators for crown diameters. The α-shape algorithm requires to define the radius parameter &lt;i&gt;R&lt;/i&gt;. In this study we investigated how sensitive are the results to the radius size, by comparing the results obtained with various settings of the R with reference values of estimated parameters from field measurements. Our study area was the olive orchard located in the Castellon Province, Spain. We used a set of ALS data with an average density of 4 points&thinsp;m&lt;sip&gt;&minus;2&lt;/sup&gt;. We noticed, that there was a narrow range of the &lt;i&gt;R&lt;/i&gt; parameter, from 0.48&thinsp;m to 0.80&thinsp;m, for which all trees were detected and for which we obtained a high correlation coefficient (&gt;&thinsp;0.9) between estimated and measured values. We compared our estimates with field measurements. The RMSE of differences was 0.8&thinsp;m for the tree height, 0.5&thinsp;m for the crown base height, 0.6&thinsp;m and 0.4&thinsp;m for the longest and shorter crown diameter, respectively. The accuracy obtained with the method is thus sufficient for agricultural applications.

scholarly journals AUTOMATIC RAILWAY POWER LINE EXTRACTION USING MOBILE LASER SCANNING DATA

2016 ◽  
Vol XLI-B5 ◽  
pp. 615-619
Author(s):  
Shanxin Zhang ◽  
Cheng Wang ◽  
Zhuang Yang ◽  
Yiping Chen ◽  
Jonathan Li
Keyword(s):  
Laser Scanning ◽  
Region Growing ◽  
Point Clouds ◽  
Power Line ◽  
Power Lines ◽  
Practical Significance ◽  
Space Region ◽  
Extraction Technology ◽  
Line Extraction ◽  
Fitting Algorithm

Research on power line extraction technology using mobile laser point clouds has important practical significance on railway power lines patrol work. In this paper, we presents a new method for automatic extracting railway power line from MLS (Mobile Laser Scanning) data. Firstly, according to the spatial structure characteristics of power-line and trajectory, the significant data is segmented piecewise. Then, use the self-adaptive space region growing method to extract power lines parallel with rails. Finally use PCA (Principal Components Analysis) combine with information entropy theory method to judge a section of the power line whether is junction or not and which type of junction it belongs to. The least squares fitting algorithm is introduced to model the power line. An evaluation of the proposed method over a complicated railway point clouds acquired by a RIEGL VMX450 MLS system shows that the proposed method is promising.

scholarly journals AUTOMATIC RAILWAY POWER LINE EXTRACTION USING MOBILE LASER SCANNING DATA

2016 ◽  
Vol XLI-B5 ◽  
pp. 615-619 ◽  
Author(s):  
Shanxin Zhang ◽  
Cheng Wang ◽  
Zhuang Yang ◽  
Yiping Chen ◽  
Jonathan Li
Keyword(s):  
Laser Scanning ◽  
Region Growing ◽  
Point Clouds ◽  
Power Line ◽  
Power Lines ◽  
Practical Significance ◽  
Space Region ◽  
Extraction Technology ◽  
Line Extraction ◽  
Fitting Algorithm

Research on power line extraction technology using mobile laser point clouds has important practical significance on railway power lines patrol work. In this paper, we presents a new method for automatic extracting railway power line from MLS (Mobile Laser Scanning) data. Firstly, according to the spatial structure characteristics of power-line and trajectory, the significant data is segmented piecewise. Then, use the self-adaptive space region growing method to extract power lines parallel with rails. Finally use PCA (Principal Components Analysis) combine with information entropy theory method to judge a section of the power line whether is junction or not and which type of junction it belongs to. The least squares fitting algorithm is introduced to model the power line. An evaluation of the proposed method over a complicated railway point clouds acquired by a RIEGL VMX450 MLS system shows that the proposed method is promising.

scholarly journals A Hierarchical Clustering Method to Repair Gaps in Point Clouds of Powerline Corridor for Powerline Extraction

2021 ◽  
Vol 13 (8) ◽  
pp. 1502
Author(s):  
Yongzhao Fan ◽  
Rong Zou ◽  
Xiaoyun Fan ◽  
Rendong Dong ◽  
Mengyou Xie
Keyword(s):  
Hierarchical Clustering ◽  
Laser Scanning ◽  
Point Clouds ◽  
Test Results ◽  
Clustering Method ◽  
Point Density ◽  
Slope Change ◽  
Neighborhood Relations ◽  
Scanning Systems ◽  
Night Period

Powerline detection is becoming a significant issue for powerline monitoring and maintenance, which further ensures transmission security. As an efficient method, laser scanning has attracted considerable attention in powerline detection for its high precision and robustness during the night period. However, due to occlusion and varying point density, gaps will appear in scans and greatly influence powerline detection by over–clustering, insufficient extraction, or misclassification in existing methods. Moreover, this situation will be worse in terrestrial laser scanning (TLS), because TLS suffers more from gaps due to its unique ground–based scanning mode compared to other laser scanning systems. Thereby, this paper explores a robust method to repair gaps for extracting powerlines from TLS data. Firstly, a hierarchical clustering method is used to extract the powerlines. During the clustering, gaps are repaired based on neighborhood relations of powerline candidates, and repaired gaps can create continuous neighborhood relations that ensure the execution of the clustering method in return. Test results show that the hierarchical clustering method is robust in powerline extraction with repaired gaps. Secondly, reconstruction is performed for further detection. Pylon–powerline connections are found by the slope change method, and powerlines with multi–span are successfully fitted using these connections. Experiment shows that it is feasible to find connections for multi–span reconstruction.

scholarly journals Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks

2021 ◽  
Vol 13 (11) ◽  
pp. 2135
Author(s):  
Jesús Balado ◽  
Pedro Arias ◽  
Henrique Lorenzo ◽  
Adrián Meijide-Rodríguez
Keyword(s):  
Laser Scanning ◽  
Ambient Noise ◽  
Point Clouds ◽  
Density Variation ◽  
Density Reduction ◽  
Manual Intervention ◽  
Point Density ◽  
Disturbance Analysis ◽  
Street Furniture

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

scholarly journals Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

2021 ◽  
Vol 13 (3) ◽  
pp. 507
Author(s):  
Tasiyiwa Priscilla Muumbe ◽  
Jussi Baade ◽  
Jenia Singh ◽  
Christiane Schmullius ◽  
Christian Thau
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Spatial Scales ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Parameter Extraction ◽  
Airborne Lidar ◽  
Special Focus ◽  
Vegetation Parameter

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.

Sign in / Sign up

Export Citation Format

Share Document