Automatic Object Extraction from Electrical Substation  Point Clouds

According to the Department of Energy of the USA, today’s electrical distribution system is 97.97% reliable. However, power outages and interruptions still impact many people. Many power outages are caused by animals coming into contact with the conductive elements of the electrical substations. This can be prevented by covering the conductive electrical objects with insulating materials. The design of these custom-built insulating covers requires a 3D as-built plan of the substation. This research aims to develop automated methods to create such a 3D as-built plan using terrestrial LiDAR data for which objects first need to be recognized in the LiDAR point clouds. This paper reports on the application of a new algorithm for the segmentation of planar surfaces found at electrical substations. The proposed approach is a region growing method that aggregates points based on their proximity to each other and their neighbourhood dispersion direction. PCA (principal components analysis) is also employed to segment planar surfaces in the electrical substation. In this research two different laser scanners, Leica HDS 6100 and Faro Focus3D, were utilized to scan an electrical substation in Airdrie, a city located in north of Calgary, Canada. In this research, three subsets incorporating one subset of Leica dataset with approximately 1.7 million points and two subsets of the Faro dataset with 587 and 79 thousand points were utilized. The performance of our proposed method is compared with the performance of PCA by performing check point analysis and investigation of computational speed. Both methods managed to detect a great proportion of planar points (about 70%). However, the proposed method slightly outperformed PCA. 95% of the points that were segmented by both methods as planar points did actually lie on a planar surface. This exhibits the high ability of both methods to identify planar points. The results also indicate that the computational speed of our method is superior to that of PCA by 50%. It is concluded that our proposed method achieves better results with higher computational speed than PCA in the segmentation of planar surfaces.

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Eigenvalue and graph-based object extraction from mobile laser scanning point clouds

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-ii-5-w2-55-2013 ◽

2013 ◽

Vol II-5/W2 ◽

pp. 55-60 ◽

Cited By ~ 11

Author(s):

M. Bremer ◽

V. Wichmann ◽

M. Rutzinger

Keyword(s):

Laser Scanning ◽

Point Clouds ◽

Object Extraction

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Automated Road Curb Break Lines Extraction from Mobile LiDAR Point Clouds

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8110476 ◽

2019 ◽

Vol 8 (11) ◽

pp. 476 ◽

Cited By ~ 2

Author(s):

Luis Gézero ◽

Carlos Antunes

Keyword(s):

Extraction Method ◽

Point Cloud ◽

Reliable Method ◽

Point Clouds ◽

Autonomous Driving ◽

Light Detection And Ranging ◽

Object Extraction ◽

Quality Value ◽

Occlusion Effect ◽

Reference Manual

In last decades, Mobile Light Detection And Ranging (LiDAR) systems were revealed to be an efficient and reliable method to collect dense and precise point clouds. The challenge now faced by researchers is the automatic object extraction from those point clouds, such as the curb break lines, which are essential to road rehabilitation projects and autonomous driving. Throughout this work, an efficient method to extract road curb break lines from mobile LiDAR point clouds is presented. The proposed method was based on the system working principles instead of an algorithmic application over the cloud as a mass of points. The point cloud was decomposed in the original sensor scan profiles. Then, a GPS epoch versus trajectory distance was used to eliminate most non-ground points. Finally, through a vertical monotone chain decomposition, candidate point arrays were created and the curb break lines are formed. The proposed method was shown to be able to avoid the occlusion effect caused by undergrowth. The method allows for distinguishing between right and left curbs and works on curved curbs. Both top and bottom tridimensional break lines were extracted. When compared with a reference manual method, in the tested dataset, the proposed method allowed for a decrease in the curb break lines extraction time from 25 min to less than 30 s. The extraction method provided completeness and correctness rates above 95% and 97%, respectively, and a quality value higher than 93%.

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A Taxonomy for Depicting Geospatial Deviations of Facilities Extracted through Comparisons between Point Clouds and Building Information Models

Computing in Civil Engineering (2012) ◽

10.1061/9780784412343.0062 ◽

2012 ◽

Cited By ~ 2

Author(s):

M. Eybpoosh ◽

B. Akinci ◽

M. Bergés

Keyword(s):

Point Clouds ◽

Information Models ◽

Building Information ◽

Building Information Models

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Mitigation and Monitoring of Structural Distress in the Whitely Electrical Substation Due to Mine Subsidence

Electrical Transmission and Substation Structures 2012 ◽

10.1061/9780784412657.012 ◽

2012 ◽

Author(s):

Michael Horn ◽

Paul Cass ◽

Enrique Bazan-Zurita

Keyword(s):

Mine Subsidence ◽

Electrical Substation

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

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2021.65.1.01050110.2352/j.imagingsci.technol.2021.65.1.010501 ◽

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.

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