Surface Reconstruction and Volume Calculation of Grain Pile Based on Point Cloud Information from Multiple Viewpoints
Accurate estimation of grain volume in storage silos is critical for intelligent monitoring and management. However, traditional image-based methods often struggle under complex lighting conditions, resulting in incomplete surface reconstruction and reduced measurement accuracy. To address these lim...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/11/1208 |
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| author | Lingmin Yang Cheng Ran Ziqing Yu Feng Han Wenfu Wu |
| author_facet | Lingmin Yang Cheng Ran Ziqing Yu Feng Han Wenfu Wu |
| author_sort | Lingmin Yang |
| collection | DOAJ |
| description | Accurate estimation of grain volume in storage silos is critical for intelligent monitoring and management. However, traditional image-based methods often struggle under complex lighting conditions, resulting in incomplete surface reconstruction and reduced measurement accuracy. To address these limitations, we propose a B-spline Interpolation and Clustered Means (BICM) method, which fuses multi-view point cloud data captured by RGB-D cameras to enable robust 3D surface reconstruction and precise volume estimation. By incorporating point cloud splicing, down-sampling, clustering, and 3D B-spline interpolation, the proposed method effectively mitigates issues such as surface notches and misalignment, significantly enhancing the accuracy of grain pile volume calculations across different viewpoints and sampling resolutions. The results of this study show that a volumetric measurement error of less than 5% can be achieved using an RGB-D camera located at two orthogonal viewpoints in combination with the BICM method, and the error can be further reduced to 1.25% when using four viewpoints. In addition to providing rapid inventory assessment of grain stocks, this approach also generates accurate local maps for the autonomous navigation of grain silo robots, thereby advancing the level of intelligent management within grain storage facilities. |
| format | Article |
| id | doaj-art-6d08799edbd44adfa6340f7cd14ea456 |
| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-6d08799edbd44adfa6340f7cd14ea4562025-08-20T02:23:43ZengMDPI AGAgriculture2077-04722025-05-011511120810.3390/agriculture15111208Surface Reconstruction and Volume Calculation of Grain Pile Based on Point Cloud Information from Multiple ViewpointsLingmin Yang0Cheng Ran1Ziqing Yu2Feng Han3Wenfu Wu4College of Biological and Agricultural Engineering, Jilin University, Changchun 130025, ChinaCollege of Biological and Agricultural Engineering, Jilin University, Changchun 130025, ChinaCollege of Biological and Agricultural Engineering, Jilin University, Changchun 130025, ChinaCollege of Biological and Agricultural Engineering, Jilin University, Changchun 130025, ChinaCollege of Biological and Agricultural Engineering, Jilin University, Changchun 130025, ChinaAccurate estimation of grain volume in storage silos is critical for intelligent monitoring and management. However, traditional image-based methods often struggle under complex lighting conditions, resulting in incomplete surface reconstruction and reduced measurement accuracy. To address these limitations, we propose a B-spline Interpolation and Clustered Means (BICM) method, which fuses multi-view point cloud data captured by RGB-D cameras to enable robust 3D surface reconstruction and precise volume estimation. By incorporating point cloud splicing, down-sampling, clustering, and 3D B-spline interpolation, the proposed method effectively mitigates issues such as surface notches and misalignment, significantly enhancing the accuracy of grain pile volume calculations across different viewpoints and sampling resolutions. The results of this study show that a volumetric measurement error of less than 5% can be achieved using an RGB-D camera located at two orthogonal viewpoints in combination with the BICM method, and the error can be further reduced to 1.25% when using four viewpoints. In addition to providing rapid inventory assessment of grain stocks, this approach also generates accurate local maps for the autonomous navigation of grain silo robots, thereby advancing the level of intelligent management within grain storage facilities.https://www.mdpi.com/2077-0472/15/11/1208grain pilesurface reconstructionvolume calculationBICMmultiple viewpoints |
| spellingShingle | Lingmin Yang Cheng Ran Ziqing Yu Feng Han Wenfu Wu Surface Reconstruction and Volume Calculation of Grain Pile Based on Point Cloud Information from Multiple Viewpoints Agriculture grain pile surface reconstruction volume calculation BICM multiple viewpoints |
| title | Surface Reconstruction and Volume Calculation of Grain Pile Based on Point Cloud Information from Multiple Viewpoints |
| title_full | Surface Reconstruction and Volume Calculation of Grain Pile Based on Point Cloud Information from Multiple Viewpoints |
| title_fullStr | Surface Reconstruction and Volume Calculation of Grain Pile Based on Point Cloud Information from Multiple Viewpoints |
| title_full_unstemmed | Surface Reconstruction and Volume Calculation of Grain Pile Based on Point Cloud Information from Multiple Viewpoints |
| title_short | Surface Reconstruction and Volume Calculation of Grain Pile Based on Point Cloud Information from Multiple Viewpoints |
| title_sort | surface reconstruction and volume calculation of grain pile based on point cloud information from multiple viewpoints |
| topic | grain pile surface reconstruction volume calculation BICM multiple viewpoints |
| url | https://www.mdpi.com/2077-0472/15/11/1208 |
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