A 2.5-D Substation Terrestrial Laser Scanning Network Planning Algorithm Based on Ray Weighting

Terrestrial laser scanning (TLS) network planning plays a crucial role in efficiently and accurately capturing point clouds of substation equipment. However, due to the diversity of equipment types and significant occlusions in substation environments, along with the need to position scans on roads for construction safety, obtaining high-quality point clouds with fewer scanners remains a challenge. To address this issue, we propose a 2.5-D TLS scanning network planning method for substations on the basis of ray weighting. First, we sample the substation scene model via triangular mesh. Next, we perform vertical slicing of the resulting point clouds. Then, we extract the bounding boxes of the sliced point clouds in the XY plane and stack them to generate the 2.5-D models. Visibility ray weights are calculated on the basis of propagation distance and equipment size, leading to the construction of a global topological visibility map under the constraint of the incidence angle. This map drives the network layout by representing changes in information gain. By applying this method, TLS networks are generated for two substation model datasets, followed by accuracy validation. The results demonstrate that, compared with other methods, the proposed approach reduces the number of scan setups by 3–5, increases by 7.4% –8.9% in the coverage, and improves the proportion of point clouds meeting the quality standard by 8.8% –9.1%. This method effectively captures high-quality point clouds in substation scenes and holds significant potential for TLS network planning, providing essential methods and data support for substation scene scanning.

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Publication Details

Journal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.1109/jstars.2025.3587040
Primary Topic
Advanced Decision-Making Techniques
Type
article
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article

A 2.5-D Substation Terrestrial Laser Scanning Network Planning Algorithm Based on Ray Weighting

Shaobo Xia, Sheng Nie, Pu Wang, Huachen Zhao et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Advanced Decision-Making Techniques
article

A 2.5-D Substation Terrestrial Laser Scanning Network Planning Algorithm Based on Ray Weighting

Shaobo Xia, Sheng Nie, Pu Wang, Huachen Zhao, Meng Du, Cheng Wang
article en

Abstract

Terrestrial laser scanning (TLS) network planning plays a crucial role in efficiently and accurately capturing point clouds of substation equipment. However, due to the diversity of equipment types and significant occlusions in substation environments, along with the need to position scans on roads for construction safety, obtaining high-quality point clouds with fewer scanners remains a challenge. To address this issue, we propose a 2.5-D TLS scanning network planning method for substations on the basis of ray weighting. First, we sample the substation scene model via triangular mesh. Next, we perform vertical slicing of the resulting point clouds. Then, we extract the bounding boxes of the sliced point clouds in the XY plane and stack them to generate the 2.5-D models. Visibility ray weights are calculated on the basis of propagation distance and equipment size, leading to the construction of a global topological visibility map under the constraint of the incidence angle. This map drives the network layout by representing changes in information gain. By applying this method, TLS networks are generated for two substation model datasets, followed by accuracy validation. The results demonstrate that, compared with other methods, the proposed approach reduces the number of scan setups by 3–5, increases by 7.4% –8.9% in the coverage, and improves the proportion of point clouds meeting the quality standard by 8.8% –9.1%. This method effectively captures high-quality point clouds in substation scenes and holds significant potential for TLS network planning, providing essential methods and data support for substation scene scanning.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingVol. 18
Chinese Academy of Sciences (CN), Beijing Institute of Big Data Research (CN), Aerospace Information Research Institute (CN), Changsha University of Science and Technology (CN), Tsinghua University (CN)
Openalex Percentile: Top 75%
Advanced Decision-Making Techniques
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