Position Normalization of Propellant Grain Point Clouds
Point cloud data obtained from scanning propellant grains with 3D scanning equipment exhibit positional uncertainty in space, posing significant challenges for calculating the relevant parameters of the propellant grains. Therefore, it is essential to normalize the position of each propellant grain’...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/11/10/859 |
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| author | Junchao Wang Fengnian Tian Renfu Li Zhihui Li Bin Zhang Xuelong Si |
| author_facet | Junchao Wang Fengnian Tian Renfu Li Zhihui Li Bin Zhang Xuelong Si |
| author_sort | Junchao Wang |
| collection | DOAJ |
| description | Point cloud data obtained from scanning propellant grains with 3D scanning equipment exhibit positional uncertainty in space, posing significant challenges for calculating the relevant parameters of the propellant grains. Therefore, it is essential to normalize the position of each propellant grain’s point cloud. This paper proposes a normalization algorithm for propellant grain point clouds, consisting of two stages, coarse normalization and fine normalization, to achieve high-precision transformations of the point clouds. In the coarse normalization stage, a layer-by-layer feature points detection scheme based on k-dimensional trees (KD-tree) and k-means clustering (k-means) is designed to extract feature points from the propellant grain point cloud. In the fine normalization stage, a rotation angle compensation scheme is proposed to align the fitted symmetry axis of the propellant grain point cloud with the coordinate axes. Finally, comparative experiments with iterative closest point (ICP) and random sample consensus (RANSAC) validate the efficiency of the proposed normalization algorithm. |
| format | Article |
| id | doaj-art-22bc32ca47ad4bf7a6cf26eec97389bd |
| institution | OA Journals |
| issn | 2226-4310 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-22bc32ca47ad4bf7a6cf26eec97389bd2025-08-20T02:11:01ZengMDPI AGAerospace2226-43102024-10-01111085910.3390/aerospace11100859Position Normalization of Propellant Grain Point CloudsJunchao Wang0Fengnian Tian1Renfu Li2Zhihui Li3Bin Zhang4Xuelong Si5School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaHubei Key Laboratory of Advanced Aerospace Propulsion Technology, System Design Institute of Hubei Aerospace Technology Academy, Wuhan 430000, ChinaHubei Key Laboratory of Advanced Aerospace Propulsion Technology, System Design Institute of Hubei Aerospace Technology Academy, Wuhan 430000, ChinaHubei Key Laboratory of Advanced Aerospace Propulsion Technology, System Design Institute of Hubei Aerospace Technology Academy, Wuhan 430000, ChinaPoint cloud data obtained from scanning propellant grains with 3D scanning equipment exhibit positional uncertainty in space, posing significant challenges for calculating the relevant parameters of the propellant grains. Therefore, it is essential to normalize the position of each propellant grain’s point cloud. This paper proposes a normalization algorithm for propellant grain point clouds, consisting of two stages, coarse normalization and fine normalization, to achieve high-precision transformations of the point clouds. In the coarse normalization stage, a layer-by-layer feature points detection scheme based on k-dimensional trees (KD-tree) and k-means clustering (k-means) is designed to extract feature points from the propellant grain point cloud. In the fine normalization stage, a rotation angle compensation scheme is proposed to align the fitted symmetry axis of the propellant grain point cloud with the coordinate axes. Finally, comparative experiments with iterative closest point (ICP) and random sample consensus (RANSAC) validate the efficiency of the proposed normalization algorithm.https://www.mdpi.com/2226-4310/11/10/859point cloudposition normalizationpropellantregistrationICPRANSAC |
| spellingShingle | Junchao Wang Fengnian Tian Renfu Li Zhihui Li Bin Zhang Xuelong Si Position Normalization of Propellant Grain Point Clouds Aerospace point cloud position normalization propellant registration ICP RANSAC |
| title | Position Normalization of Propellant Grain Point Clouds |
| title_full | Position Normalization of Propellant Grain Point Clouds |
| title_fullStr | Position Normalization of Propellant Grain Point Clouds |
| title_full_unstemmed | Position Normalization of Propellant Grain Point Clouds |
| title_short | Position Normalization of Propellant Grain Point Clouds |
| title_sort | position normalization of propellant grain point clouds |
| topic | point cloud position normalization propellant registration ICP RANSAC |
| url | https://www.mdpi.com/2226-4310/11/10/859 |
| work_keys_str_mv | AT junchaowang positionnormalizationofpropellantgrainpointclouds AT fengniantian positionnormalizationofpropellantgrainpointclouds AT renfuli positionnormalizationofpropellantgrainpointclouds AT zhihuili positionnormalizationofpropellantgrainpointclouds AT binzhang positionnormalizationofpropellantgrainpointclouds AT xuelongsi positionnormalizationofpropellantgrainpointclouds |