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 COMPARATIVE GEOMETRICAL INVESTIGATIONS OF HAND-HELD SCANNING SYSTEMS

2016 ◽  
Vol XLI-B5 ◽  
pp. 507-514 ◽  
Author(s):  
T. P. Kersten ◽  
H.-J. Przybilla ◽  
M. Lindstaedt ◽  
F. Tschirschwitz ◽  
M. Misgaiski-Hass
Keyword(s):  
Applied Sciences ◽  
Laser Scanning ◽  
Reference Data ◽  
Test Results ◽  
University Of Applied Sciences ◽  
Humboldt University ◽  
Comprehensive Test ◽  
Structure Sensor ◽  
Scanning Systems ◽  
Test Scenarios

An increasing number of hand-held scanning systems by different manufacturers are becoming available on the market. However, their geometrical performance is little-known to many users. Therefore the Laboratory for Photogrammetry & Laser Scanning of the HafenCity University Hamburg has carried out geometrical accuracy tests with the following systems in co-operation with the Bochum University of Applied Sciences (Laboratory for Photogrammetry) as well as the Humboldt University in Berlin (Institute for Computer Science): DOTProduct DPI-7, Artec Spider, Mantis Vision F5 SR, Kinect v1 + v2, Structure Sensor and Google’s Project Tango. In the framework of these comparative investigations geometrically stable reference bodies were used. The appropriate reference data were acquired by measurement with two structured light projection systems (AICON smartSCAN and GOM ATOS I 2M). The comprehensive test results of the different test scenarios are presented and critically discussed in this contribution.

scholarly journals Segmentation of LiDAR Data Using Multilevel Cube Code

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
So-Young Park ◽  
Dae Geon Lee ◽  
Eun Jin Yoo ◽  
Dong-Cheon Lee
Keyword(s):  
Spatial Data ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Lidar Data ◽  
Chain Code ◽  
Cloud Data ◽  
Airborne Laser ◽  
Wide Range ◽  
Scanning Systems

Light detection and ranging (LiDAR) data collected from airborne laser scanning systems are one of the major sources of spatial data. Airborne laser scanning systems have the capacity for rapid and direct acquisition of accurate 3D coordinates. Use of LiDAR data is increasing in various applications, such as topographic mapping, building and city modeling, biomass measurement, and disaster management. Segmentation is a crucial process in the extraction of meaningful information for applications such as 3D object modeling and surface reconstruction. Most LiDAR processing schemes are based on digital image processing and computer vision algorithms. This paper introduces a shape descriptor method for segmenting LiDAR point clouds using a “multilevel cube code” that is an extension of the 2D chain code to 3D space. The cube operator segments point clouds into roof surface patches, including superstructures, removes unnecessary objects, detects the boundaries of buildings, and determines model key points for building modeling. Both real and simulated LiDAR data were used to verify the proposed approach. The experiments demonstrated the feasibility of the method for segmenting LiDAR data from buildings with a wide range of roof types. The method was found to segment point cloud data effectively.

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 Performance of parameter-domain and spatial-domain pole-like feature segmentation using single and multiple terrestrial laser scans

2014 ◽  
Vol XL-5 ◽  
pp. 349-355 ◽  
Author(s):  
Z. Lari ◽  
K. Al-Durgham ◽  
A. Habib
Keyword(s):  
Urban Areas ◽  
Laser Scanning ◽  
Wide Spectrum ◽  
Three Dimensional ◽  
Point Clouds ◽  
Spatial Domain ◽  
Parameter Domain ◽  
Registration Procedure ◽  
Local Point ◽  
Point Density

Terrestrial laser scanning (TLS) systems have been established as a leading tool for the acquisition of high density three-dimensional point clouds from physical objects. The collected point clouds by these systems can be utilized for a wide spectrum of object extraction, modelling, and monitoring applications. Pole-like features are among the most important objects that can be extracted from TLS data especially those acquired in urban areas and industrial sites. However, these features cannot be completely extracted and modelled using a single TLS scan due to significant local point density variations and occlusions caused by the other objects. Therefore, multiple TLS scans from different perspectives should be integrated through a registration procedure to provide a complete coverage of the pole-like features in a scene. To date, different segmentation approaches have been proposed for the extraction of pole-like features from either single or multiple-registered TLS scans. These approaches do not consider the internal characteristics of a TLS point cloud (local point density variations and noise level in data) and usually suffer from computational inefficiency. To overcome these problems, two recently-developed PCA-based parameter-domain and spatial-domain approaches for the segmentation of pole-like features are introduced, in this paper. Moreover, the performance of the proposed segmentation approaches for the extraction of pole-like features from a single or multiple-registered TLS scans is investigated in this paper. The alignment of the utilized TLS scans is implemented using an Iterative Closest Projected Point (ICPP) registration procedure. Qualitative and quantitative evaluation of the extracted pole-like features from single and multiple-registered TLS scans, using both of the proposed segmentation approaches, is conducted to verify the extraction of more complete pole-like features using multipleregistered TLS scans.

scholarly journals POTENTIAL OF FULL WAVEFORM AIRBORNE LASER SCANNING DATA FOR URBAN AREA CLASSIFICATION – TRANSFER OF CLASSIFICATION APPROACHES BETWEEN MISSIONS

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1317-1323 ◽  
Author(s):  
G. Tran ◽  
D. Nguyen ◽  
M. Milenkovic ◽  
N. Pfeifer
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Surface Model ◽  
Knowledge Based ◽  
Full Waveform ◽  
Airborne Laser ◽  
Point Density ◽  
Area Classification

Full-waveform (FWF) LiDAR (Light Detection and Ranging) systems have their advantage in recording the entire backscattered signal of each emitted laser pulse compared to conventional airborne discrete-return laser scanner systems. The FWF systems can provide point clouds which contain extra attributes like amplitude and echo width, etc. In this study, a FWF data collected in 2010 for Eisenstadt, a city in the eastern part of Austria was used to classify four main classes: buildings, trees, waterbody and ground by employing a decision tree. Point density, echo ratio, echo width, normalised digital surface model and point cloud roughness are the main inputs for classification. The accuracy of the final results, correctness and completeness measures, were assessed by comparison of the classified output to a knowledge-based labelling of the points. Completeness and correctness between 90% and 97% was reached, depending on the class. While such results and methods were presented before, we are investigating additionally the transferability of the classification method (features, thresholds …) to another urban FWF lidar point cloud. Our conclusions are that from the features used, only echo width requires new thresholds. A data-driven adaptation of thresholds is suggested.

scholarly journals Fusion of range-based data and image-based datasets for efficient documentation of cultural heritage objects and sites

2015 ◽  
Vol XL-5/W7 ◽  
pp. 277-281 ◽  
Author(s):  
J. L. Lerma ◽  
M. Cabrelles ◽  
S. Navarro
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
3D Models ◽  
Indirect Methods ◽  
Recording Project ◽  
Dense Point ◽  
The Times ◽  
Cultural Heritage Objects ◽  
Scanning Systems ◽  
Direct And Indirect Methods

Nowadays it is possible to measure accurately dense point clouds either with aerial/terrestrial laser scanning systems or with imagebased solutions (namely based on photogrammetric computer vision algorithms such as structure-from-motion (SfM)), from which highly detailed 3D models can be achieved. Besides, direct tools in the form of simple devices such as rulers, compass and plumblines are usually required in simple metric surveys, as well as high-end surveying and geodetic instruments such as robotized imagebased total stations and GNSS (probably to a lesser degree but still required) to set the archaeological/architectural recording project in a global reference frame. With all this gamut of image-based and range-based sensors and datasets (in the form of coordinates, point clouds or 3D models), in different coordinate systems (most of the times local for each device), lack of uniform scale, orientation and levelling, the fusion of data tends to be cumbersome. This paper presents an efficient way to fuse and merge different datasets in the form of point clouds/3D models and geodetic/UTM coordinates. The new developed 3DVEM – Register GEO software is able to handle datasets coming from both direct and indirect methods in order to provide unified and precise deliverables.

scholarly journals STRUCTURAL SEGMENTATION OF POINT CLOUDS WITH VARYING DENSITY BASED ON MULTI-SIZE SUPERVOXELS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 389-396
Author(s):  
Y. Li ◽  
B. Wu
Keyword(s):  
Laser Scanning ◽  
Large Data ◽  
Point Clouds ◽  
Semantic Interpretation ◽  
Minimum Size ◽  
Structural Components ◽  
Segmentation Method ◽  
Size Constraints ◽  
Point Density ◽  
Data Volume

<p><strong>Abstract.</strong> Ground objects can be regarded as a combination of structures of different geometries. Generally, the structural geometries can be grouped into linear, planar and scatter shapes. A good segmentation of objects into different structures can help to interpret the scanned scenes and provide essential clues for subsequent semantic interpretation. This is particularly true for the terrestrial static and mobile laser scanning data, where the geometric structures of objects are presented in detail due to the close scanning distances. In consideration of the large data volume and the large variation in point density of such point clouds, this paper presents a structural segmentation method of point clouds to efficiently decompose the ground objects into different structural components based on supervoxels of multiple sizes. First, supervoxels are generated with sizes adaptive to the point density with minimum occupied points and minimum size constraints. Then, the multi-size supervoxels are clustered into different components based on a structural labelling result obtained via Markov random field. Two datasets including terrestrial and mobile laser scanning point clouds were used to evaluate the performance of the proposed method. The results indicate that the proposed method can effectively and efficiently classify the point clouds into structurally meaningful segments with overall accuracies higher than 96%, even with largely varying point density.</p>

scholarly journals A COMPARISON OF SINGLE PHOTON AND FULL WAVEFORM LIDAR

2019 ◽  
Vol IV-2/W5 ◽  
pp. 397-404 ◽  
Author(s):  
G. Mandlburger ◽  
H. Lehner ◽  
N. Pfeifer
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Single Photon ◽  
Full Waveform ◽  
Point Density ◽  
Satisfactory Precision ◽  
Waveform Lidar ◽  
Full Waveform Lidar ◽  
The City ◽  
Scanning Systems

<p><strong>Abstract.</strong> Single photon sensitive LiDAR sensors are currently competing with conventional multi-photon laser scanning systems. The advantage of the prior is the potentially higher area coverage performance, which comes at the price of an increased outlier rate and a lower ranging accuracy. In this contribution, the principles of both technologies are reviewed with special emphasis on their respective properties. In addition, a comparison of Single Photon LiDAR (SPL) and FullWaveform LiDAR data acquired in July and September 2018 in the City of Vienna are presented. From data analysis we concluded that (i) less flight strips are needed to cover the same area with comparable point density with SPL, (ii) the sharpness of the resulting 3D point cloud is higher for the waveform LiDAR dataset, (iii) SPL exhibits moderate vegetation penetration under leaf-on conditions, and (iv) the dispersion of the SPL point cloud assessed in smooth horizontal surface parts competes with waveform LiDAR but is higher by a factor of 2&amp;ndash;3 for inclined and grassy surfaces, respectively. Still, SPL yielded satisfactory precision measures mostly below 10&amp;thinsp;cm.</p>

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