Automatic 3D reconstruction of electrical substation scene from LiDAR point cloud

Wu, Qiaoyun; Yang, Hong-Chang; Wei, Mingqiang; Remil, Oussama; Wang, Bo; Wang, Jun

doi:10.1016/j.isprsjprs.2018.04.024

Cited by 33 publications

(13 citation statements)

References 34 publications

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“…A 3D model can be built by a light detection and ranging (LIDAR) system after obtaining 3D laser point clouds of electrical equipment in a substation. These models are widely used in the power industry during grid construction and operation [1]- [3]. In the substation modeling field, the solutions commonly used in production are often manually compared to the ground truth by combining individual field photos and CAD models, which is a burdensome and difficult task because of the complex structures.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Voxel-Based Segmentation Algorithm Based on Hierarchical Clustering to Extract LIDAR Power Equipment Data in Transformer Substations

et al. 2020

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Light detection and ranging (LIDAR) scanning is a common method of substation scene modeling that extracts point clouds of electrical equipment from the point cloud scene of a substation. The extraction effect is limited by uncertainty regarding the noise level, nonuniform point cloud density, and the computational complexity. In this paper, we propose a point cloud extraction solution for electrical equipment models. First, a statistical analysis of substation ground elevation is performed to obtain the point clouds of devices at the feature height and remove large numbers of redundant underground point clouds. Second, based on the statistically derived power equipment feature heights, the point cloud data are sliced according to the featured elevation intervals. Based on voxelization, the point cloud slices are then clustered using horizontal hierarchical clustering. The clustering results at different elevation intervals are then reclustered using vertical hierarchical clustering. Finally, we use filters combined with the DBSCAN algorithm to perform fine segmentation on the point cloud data. The results show that our slice clustering approach reduces the computational burden involved in point cloud processing, and the comprehensive clustering strategy ensures the accuracy of the clustering results.INDEX TERMS clustering methods, point cloud segmentation, smart grid, substation modeling,

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Section: Introductionmentioning

confidence: 99%

An Efficient Voxel-Based Segmentation Algorithm Based on Hierarchical Clustering to Extract LIDAR Power Equipment Data in Transformer Substations

et al. 2020

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“…At present, the airborne Light Detection and Ranging (LiDAR) and aerial photogrammetry systems are the main sources for obtaining a large amount of earth observation data. Combining the 3D information contained in LiDAR data with the rich semantic information in aerial imagery plays an important role in many applications such as building extraction (Awrangjeb et al, 2013), Change detection (Qin and Gruen, 2014), 3D reconstruction (Wu et al, 2018), etc. Image registration aims to align two or more images, which is a prerequisite step to combining LiDAR data and aerial images.…”

Section: Introductionmentioning

confidence: 99%

Fast and Robust Registration of Aerial Images and Lidar Data Based on Structural Features and 3d Phase Correlation

Zhu

Yang

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Co-Registration of aerial imagery and Light Detection and Ranging (LiDAR) data is quilt challenging because the different imaging mechanism causes significant geometric and radiometric distortions between such data. To tackle the problem, this paper proposes an automatic registration method based on structural features and three-dimension (3D) phase correlation. In the proposed method, the LiDAR point cloud data is first transformed into the intensity map, which is used as the reference image. Then, we employ the Fast operator to extract uniformly distributed interest points in the aerial image by a partition strategy and perform a local geometric correction by using the collinearity equation to eliminate scale and rotation difference between images. Subsequently, a robust structural feature descriptor is build based on dense gradient features, and the 3D phase correlation is used to detect control points (CPs) between aerial images and LiDAR data in the frequency domain, where the image matching is accelerated by the 3D Fast Fourier Transform (FFT). Finally, the obtained CPs are employed to correct the exterior orientation elements, which is used to achieve co-registration of aerial images and LiDAR data. Experiments with two datasets of aerial images and LiDAR data show that the proposed method is much faster and more robust than state of the art methods.

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“…There are some shortcomings of such work including the high intensity of operation, long operation period, inaccurate data acquisition, low accuracy, low reuse, and difficulty of working in complex terrain areas. Researchers have proposed the use of various remote sensing approaches such as synthetic aperture radar (SAR) [2,3], thermal images [4], optical images [5,6], airborne laser scanning (ALS) [7,8], and unmanned aerial vehicle (UAV) [9] to identify power facilities. Matikainen et al [10] give a detailed overview of different sensors in the application of power facility detection.…”

Section: Introductionmentioning

confidence: 99%

Classification of Power Facility Point Clouds from Unmanned Aerial Vehicles Based on Adaboost and Topological Constraints

Liu

Aleksandrov

Zlatanova

et al. 2019

Sensors

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Machine learning algorithms can be well suited to LiDAR point cloud classification, but when they are applied to the point cloud classification of power facilities, many problems such as a large number of computational features and low computational efficiency can be encountered. To solve these problems, this paper proposes the use of the Adaboost algorithm and different topological constraints. For different objects, the top five features with the best discrimination are selected and combined into a strong classifier by the Adaboost algorithm, where coarse classification is performed. For power transmission lines, the optimum scales are selected automatically, and the coarse classification results are refined. For power towers, it is difficult to distinguish the tower from vegetation points by only using spatial features due to the similarity of their proposed key features. Therefore, the topological relationship between the power line and power tower is introduced to distinguish the power tower from vegetation points. The experimental results show that the classification of power transmission lines and power towers by our method can achieve the accuracy of manual classification results and even be more efficient.

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Automatic 3D reconstruction of electrical substation scene from LiDAR point cloud

Cited by 33 publications

References 34 publications

An Efficient Voxel-Based Segmentation Algorithm Based on Hierarchical Clustering to Extract LIDAR Power Equipment Data in Transformer Substations

An Efficient Voxel-Based Segmentation Algorithm Based on Hierarchical Clustering to Extract LIDAR Power Equipment Data in Transformer Substations

Fast and Robust Registration of Aerial Images and Lidar Data Based on Structural Features and 3d Phase Correlation

Classification of Power Facility Point Clouds from Unmanned Aerial Vehicles Based on Adaboost and Topological Constraints

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