Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining
The calculation of volumes for irregular bodies holds significant relevance across various production processes. This spans tasks such as evaluating the growth status of crops and fruits, conducting morphological analyses of spatial objects based on volume parameters, and estimating quantities for e...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/20/5006 |
| _version_ | 1797572385152434176 |
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| author | Xiaoliang Meng Tianyi Wang Dayu Cheng Wensong Su Peng Yao Xiaoli Ma Meizhen He |
| author_facet | Xiaoliang Meng Tianyi Wang Dayu Cheng Wensong Su Peng Yao Xiaoli Ma Meizhen He |
| author_sort | Xiaoliang Meng |
| collection | DOAJ |
| description | The calculation of volumes for irregular bodies holds significant relevance across various production processes. This spans tasks such as evaluating the growth status of crops and fruits, conducting morphological analyses of spatial objects based on volume parameters, and estimating quantities for earthwork and excavation. While methods like drainage, surface reconstruction, and triangulation suffice for smaller irregular bodies, larger ones introduce heightened complexity. Technological advancements, such as UAV photogrammetry and LiDAR, have introduced efficient point cloud data acquisition methods, bolstering precision and efficiency in calculating volumes for substantial irregular bodies. Notably, open-pit mines, characterized by their dynamic surface alterations, exemplify the challenges posed by large irregular bodies. Ensuring accurate excavation quantity calculations in such mines is pivotal, impacting operational considerations, acceptance, as well as production cost management and project oversight. Thus, this study employs UAV-acquired point cloud data from open-pit mines as a case study. In practice, calculating volumes for substantial irregular bodies often relies on the point cloud slicing method. However, this approach grapples with distinguishing multi-contour boundaries, leading to inaccuracies. To surmount this hurdle, this paper introduces an enhanced point cloud slicing method. The methodology involves segmenting point cloud data at fixed intervals, followed by the segmentation of slice contours using the Euclidean clustering method. Subsequently, the concave hull algorithm extracts the contour polygons of each slice. The final volume calculation involves multiplying the area of each polygon by the spacing and aggregating these products. To validate the efficacy of our approach, we employ model-derived volumes as benchmarks, comparing errors arising from both the traditional slicing method and our proposed technique. Experimental outcomes underscore the superiority of our point cloud volume calculation method, manifesting in an average relative error of 1.17%, outperforming the conventional point cloud slicing method in terms of accuracy. |
| first_indexed | 2024-03-10T20:55:31Z |
| format | Article |
| id | doaj.art-40c40539b2b34b958780a8e15624d5b8 |
| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-03-10T20:55:31Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj.art-40c40539b2b34b958780a8e15624d5b82023-11-19T17:59:29ZengMDPI AGRemote Sensing2072-42922023-10-011520500610.3390/rs15205006Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit MiningXiaoliang Meng0Tianyi Wang1Dayu Cheng2Wensong Su3Peng Yao4Xiaoli Ma5Meizhen He6School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Mining and Geomatics Engineering, Hebei University of Engineering, Handan 056038, ChinaSchool of Mining and Geomatics Engineering, Hebei University of Engineering, Handan 056038, ChinaGuangxi Taiwei Information Technology Co., Ltd., Guilin 541000, ChinaLands and Resource Department of Guangdong Province, Surveying and Mapping Institute, Guangzhou 510500, ChinaLands and Resource Department of Guangdong Province, Surveying and Mapping Institute, Guangzhou 510500, ChinaThe calculation of volumes for irregular bodies holds significant relevance across various production processes. This spans tasks such as evaluating the growth status of crops and fruits, conducting morphological analyses of spatial objects based on volume parameters, and estimating quantities for earthwork and excavation. While methods like drainage, surface reconstruction, and triangulation suffice for smaller irregular bodies, larger ones introduce heightened complexity. Technological advancements, such as UAV photogrammetry and LiDAR, have introduced efficient point cloud data acquisition methods, bolstering precision and efficiency in calculating volumes for substantial irregular bodies. Notably, open-pit mines, characterized by their dynamic surface alterations, exemplify the challenges posed by large irregular bodies. Ensuring accurate excavation quantity calculations in such mines is pivotal, impacting operational considerations, acceptance, as well as production cost management and project oversight. Thus, this study employs UAV-acquired point cloud data from open-pit mines as a case study. In practice, calculating volumes for substantial irregular bodies often relies on the point cloud slicing method. However, this approach grapples with distinguishing multi-contour boundaries, leading to inaccuracies. To surmount this hurdle, this paper introduces an enhanced point cloud slicing method. The methodology involves segmenting point cloud data at fixed intervals, followed by the segmentation of slice contours using the Euclidean clustering method. Subsequently, the concave hull algorithm extracts the contour polygons of each slice. The final volume calculation involves multiplying the area of each polygon by the spacing and aggregating these products. To validate the efficacy of our approach, we employ model-derived volumes as benchmarks, comparing errors arising from both the traditional slicing method and our proposed technique. Experimental outcomes underscore the superiority of our point cloud volume calculation method, manifesting in an average relative error of 1.17%, outperforming the conventional point cloud slicing method in terms of accuracy.https://www.mdpi.com/2072-4292/15/20/5006irregular bodiesopen-pit mineexcavation quantitypoint cloud slicing methodEuclidean clusteringconcave hull |
| spellingShingle | Xiaoliang Meng Tianyi Wang Dayu Cheng Wensong Su Peng Yao Xiaoli Ma Meizhen He Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining Remote Sensing irregular bodies open-pit mine excavation quantity point cloud slicing method Euclidean clustering concave hull |
| title | Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining |
| title_full | Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining |
| title_fullStr | Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining |
| title_full_unstemmed | Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining |
| title_short | Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining |
| title_sort | enhanced point cloud slicing method for volume calculation of large irregular bodies validation in open pit mining |
| topic | irregular bodies open-pit mine excavation quantity point cloud slicing method Euclidean clustering concave hull |
| url | https://www.mdpi.com/2072-4292/15/20/5006 |
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