scholarly journals Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds

2019 ◽  
Vol 11 (22) ◽  
pp. 2600 ◽  
Author(s):  
Ruizhuo Zhang ◽  
Bisheng Yang ◽  
Wen Xiao ◽  
Fuxun Liang ◽  
Yang Liu ◽  
...  
Keyword(s):  
Electric Power ◽  
High Voltage ◽  
Point Cloud ◽  
Power Transmission ◽  
Point Clouds ◽  
3D Models ◽  
Noise Removal ◽  
Power Lines ◽  
Cloud Data ◽  
Communication Signal

Electric power transmission and maintenance is essential for the power industry. This paper proposes a method for the efficient extraction and classification of three-dimensional (3D) targets of electric power transmission facilities based on regularized grid characteristics computed from point cloud data acquired by unmanned aerial vehicles (UAVs). First, a special hashing matrix was constructed to store the point cloud after noise removal by a statistical method, which calculated the local distribution characteristics of the points within each sparse grid. Secondly, power lines were extracted by neighboring grids’ height similarity estimation and linear feature clustering. Thirdly, by analyzing features of the grid in the horizontal and vertical directions, the transmission towers in candidate tower areas were identified. The pylon center was then determined by a vertical slicing analysis. Finally, optimization was carried out, considering the topological relationship between the line segments and pylons to refine the extraction. Experimental results showed that the proposed method was able to efficiently obtain accurate coordinates of pylon and attachments in the massive point data and to produce a reliable segmentation with an overall precision of 97%. The optimized algorithm was capable of eliminating interference from isolated tall trees and communication signal poles. The 3D geo-information of high-voltage (HV) power lines, pylons, conductors thus extracted, and of further reconstructed 3D models can provide valuable foundations for UAV remote-sensing inspection and corridor safety maintenance.

scholarly journals Wind: generating power and cooling the power lines

2020 ◽  
Vol 17 ◽  
pp. 105-108
Author(s):  
Marko Kaasik ◽  
Sander Mirme
Keyword(s):  
Wind Speed ◽  
Electric Power ◽  
High Voltage ◽  
Transmission Lines ◽  
Power Transmission ◽  
Mixed Forest ◽  
Wind Farms ◽  
Power Line ◽  
Power Lines ◽  
Local Wind

Abstract. The electric power that can be transmitted via high-voltage transmission lines is limited by the Joule heating of the conductors. In the case of coastal wind farms, the wind that produces power simultaneously contributes to the cooling of high-voltage overhead conductors. Ideally this would allow for increased power transmission or decreased dimensions and cost of the conductor wires. In this study we investigate how well the wind speed in coastal wind farms is correlated with wind along a 75 km long 330 kW power line towards inland. It is found that correlations between wind speed in coastal wind farms at turbine height and conductor-level (10 m) are remarkably lower (R=0.39–0.64) than between wind farms at distances up to 100 km from each other (R=0.76–0.97). Dense mixed forest surrounding the power line reduces both local wind speed and the correlations with coastal higher-level wind, thus making the cooling effect less reliable.

scholarly journals AUTOMATIC EXTRACTION OF POWER LINES FROM UAV LIDAR POINT CLOUDS USING A NOVEL SPATIAL FEATURE

2019 ◽  
Vol IV-2/W7 ◽  
pp. 227-234
Author(s):  
M. Zhou ◽  
K. Y. Li ◽  
J. H. Wang ◽  
C. R. Li ◽  
G. E. Teng ◽  
...  
Keyword(s):  
Point Cloud ◽  
Real Data ◽  
Point Clouds ◽  
Power Line ◽  
Power Lines ◽  
Point Cloud Data ◽  
Point Sets ◽  
Linear Discriminant ◽  
Cloud Data ◽  
Line Extraction

<p><strong>Abstract.</strong> UAV LiDAR systems have unique advantage in acquiring 3D geo-information of the targets and the expenses are very reasonable; therefore, they are capable of security inspection of high-voltage power lines. There are already several methods for power line extraction from LiDAR point cloud data. However, the existing methods either introduce classification errors during point cloud filtering, or occasionally unable to detect multiple power lines in vertical arrangement. This paper proposes and implements an automatic power line extraction method based on 3D spatial features. Different from the existing power line extraction methods, the proposed method processes the LiDAR point cloud data vertically, therefore, the possible location of the power line in point cloud data can be predicted without filtering. Next, segmentation is conducted on candidates of power line using 3D region growing method. Then, linear point sets are extracted by linear discriminant method in this paper. Finally, power lines are extracted from the candidate linear point sets based on extension and direction features. The effectiveness and feasibility of the proposed method were verified by real data of UAV LiDAR point cloud data in Sichuan, China. The average correct extraction rate of power line points is 98.18%.</p>

scholarly journals 3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

2018 ◽  
Vol 10 (9) ◽  
pp. 1412 ◽  
Author(s):  
Florent Poux ◽  
Romain Neuville ◽  
Gilles-Antoine Nys ◽  
Roland Billen
Keyword(s):  
Point Cloud ◽  
3D Model ◽  
Contextual Information ◽  
Point Clouds ◽  
3D Models ◽  
Cloud Data ◽  
Semantic Modelling ◽  
Wide Range ◽  
Virtual Stores ◽  
3D Scans

3D models derived from point clouds are useful in various shapes to optimize the trade-off between precision and geometric complexity. They are defined at different granularity levels according to each indoor situation. In this article, we present an integrated 3D semantic reconstruction framework that leverages segmented point cloud data and domain ontologies. Our approach follows a part-to-whole conception which models a point cloud in parametric elements usable per instance and aggregated to obtain a global 3D model. We first extract analytic features, object relationships and contextual information to permit better object characterization. Then, we propose a multi-representation modelling mechanism augmented by automatic recognition and fitting from the 3D library ModelNet10 to provide the best candidates for several 3D scans of furniture. Finally, we combine every element to obtain a consistent indoor hybrid 3D model. The method allows a wide range of applications from interior navigation to virtual stores.

scholarly journals DATA-DRIVEN MODELING OF BUILDING INTERIORS FROM LIDAR POINT CLOUDS

2020 ◽  
Vol V-2-2020 ◽  
pp. 395-402
Author(s):  
J. Sanchez ◽  
F. Denis ◽  
F. Dupont ◽  
L. Trassoudaine ◽  
P. Checchin
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Real Data ◽  
Point Clouds ◽  
3D Models ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Automatic Methods ◽  
Data Driven Modeling ◽  
3D Lidar

Abstract. This paper deals with 3D modeling of building interiors from point clouds captured by a 3D LiDAR scanner. Indeed, currently, the building reconstruction processes remain mostly manual. While LiDAR data have some specific properties which make the reconstruction challenging (anisotropy, noise, clutters, etc.), the automatic methods of the state-of-the-art rely on numerous construction hypotheses which yield 3D models relatively far from initial data. The choice has been done to propose a new modeling method closer to point cloud data, reconstructing only scanned areas of each scene and excluding occluded regions. According to this objective, our approach reconstructs LiDAR scans individually using connected polygons. This modeling relies on a joint processing of an image created from the 2D LiDAR angular sampling and the 3D point cloud associated to one scan. Results are evaluated on synthetic and real data to demonstrate the efficiency as well as the technical strength of the proposed method.

scholarly journals An Efficient Approach to Automatic Construction of 3D Watertight Geometry of Buildings Using Point Clouds

2021 ◽  
Vol 13 (10) ◽  
pp. 1947
Author(s):  
Yuanzhi Cai ◽  
Lei Fan
Keyword(s):  
Point Cloud ◽  
Geometric Model ◽  
Computation Time ◽  
Point Clouds ◽  
3D Models ◽  
Ray Casting ◽  
Point Cloud Data ◽  
Geometric Models ◽  
Cloud Data ◽  
Reconstruction Methods

Recent years have witnessed an increasing use of 3D models in general and 3D geometric models specifically of built environment for various applications, owing to the advancement of mapping techniques for accurate 3D information. Depending on the application scenarios, there exist various types of approaches to automate the construction of 3D building geometry. However, in those studies, less attention has been paid to watertight geometries derived from point cloud data, which are of use to the management and the simulations of building energy. To this end, an efficient reconstruction approach was introduced in this study and involves the following key steps. The point cloud data are first voxelised for the ray-casting analysis to obtain the 3D indoor space. By projecting it onto a horizontal plane, an image representing the indoor area is obtained and is used for the room segmentation. The 2D boundary of each room candidate is extracted using new grammar rules and is extruded using the room height to generate 3D models of individual room candidates. The room connection analyses are applied to the individual models obtained to determine the locations of doors and the topological relations between adjacent room candidates for forming an integrated and watertight geometric model. The approach proposed was tested using the point cloud data representing six building sites of distinct spatial confirmations of rooms, corridors and openings. The experimental results showed that accurate watertight building geometries were successfully created. The average differences between the point cloud data and the geometric models obtained were found to range from 12 to 21 mm. The maximum computation time taken was less than 5 min for the point cloud of approximately 469 million data points, more efficient than the typical reconstruction methods in the literature.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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.

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