Research on Automatic Denoising of LIDAR Point Cloud Data for Substation Equipment Based on Spatial Grid Density

Xinle Yu, Yong Du, Hao Wang, Jingsong Yao, Yuandong Wang, Xiaojun Shen

Abstract


Aiming at the shortcomings of the existing 3D point cloud data automatic extraction methods of substation equipment, which are highly dependent on big data algorithms and low efficiency, this paper proposes a 3D LIDAR point cloud data segmentation method and process based on the multidimensional subspace grid density difference. The proposed method is based on eliminating the flying spots of 3D point cloud data, and is divided into equipment point cloud data and ground point cloud data based on point cloud data characteristics for 3D real-world modeling and accurate positioning of the model; Among them, the equipment point cloud data uses a multi-dimensional density difference segmentation method. The long-distance terrain is divided in the XOY and YOZ planes, and converted into a combination of multiple small-scale scale spaces. Effective segmentation, so that automatic extraction of substation equipment can be realized; The ground point cloud data uses a single-dimensional density difference segmentation method to dilute the ground point cloud data to obtain clear positioning points. The feasibility verification results of cloud data of a UHV substation show that the proposed method can effectively suppress the noise interference of interference points, realize accurate extraction and location of substation equipment, and the algorithm has high efficiency and strong engineering application.

Keywords


substation, LIDAR point cloud data, automatic extraction


DOI
10.12783/dtetr/mcaee2020/35065

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