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

Jianlong Guo,Shan Xiong,Huben Mao,Jiang Xue,Ruiheng Li,Weixia Feng,Tengfei Hao

doi:10.1109/access.2020.3046313

Jianlong Guo, Shan Xiong + Show 5 more

Open Access

https://doi.org/10.1109/access.2020.3046313

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Abstract

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.

Highlights

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
Considering the height variations of the ground, to obtain a more precise estimation of the ground height, we count the number of substation scene point clouds (Equation 4) that fall into different height intervals and assume that the ground height corresponding to each grid is the height interval corresponding to the peak value in each histogram
The results show that changes to these parameter values in the SOR have little effect on the final device point clouds produced by our method, which indicates that our method has strong denoising ability

Summary

Introduction

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. Considering that the continuity of the spatial distribution of substation equipment point clouds is better than the continuity of the noise point cloud, the noise can be identified and removed based on this spatial discontinuity This means that the denoising of the substation point cloud scene and segmentation of the target device point cloud can be completed simultaneously using the distance information. In the rapid preliminary clustering stage, we project the 3D scene point cloud to 2D feature planes of different heights and integrate the 2D clustering results through hierarchical clustering This operation improves the computational efficiency by reducing the dimensionality from a three-dimensional to a two-dimensional clustering process but can use vertical continuity to remove noise.

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Conclusion