Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining
-
Published:2023-10-18
Issue:20
Volume:15
Page:5006
-
ISSN:2072-4292
-
Container-title:Remote Sensing
-
language:en
-
Short-container-title:Remote Sensing
Author:
Meng Xiaoliang12, Wang Tianyi1, Cheng Dayu34, Su Wensong3, Yao Peng5, Ma Xiaoli6, He Meizhen6
Affiliation:
1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China 2. Hubei Luojia Laboratory, Wuhan 430072, China 3. School of Mining and Geomatics Engineering, Hebei University of Engineering, Handan 056038, China 4. Research Center of Hebei Province Ecological Civilization and Social Governance, Hebei University of Engineering, Handan 056038, China 5. Guangxi Taiwei Information Technology Co., Ltd., Guilin 541000, China 6. Lands and Resource Department of Guangdong Province, Surveying and Mapping Institute, Guangzhou 510500, China
Abstract
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.
Funder
National Natural Science Foundation of China
Subject
General Earth and Planetary Sciences
Reference30 articles.
1. Curless, B., and Levoy, M. (1996, January 4–9). A volumetric method for building complex models from range images. Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, New Orleans, LA, USA. 2. Artzet, S., Chen, T.W., Chopard, J., Brichet, N., Mielewczik, M., Cohen-Boulakia, S., and Pradal, C. (2019). Phenomenal: An automatic open source library for 3D shoot architecture reconstruction and analysis for image-based plant phenotyping. bioRxiv, bioxriv: 805739. 3. Xu, Z., Xu, E., Wu, L., Liu, S., and Mao, Y. (2019). Registration of terrestrial laser scanning surveys using terrain-invariant regions for measuring exploitative volumes over open-pit mines. Remote Sens., 11. 4. Szostak, M., and Pająk, M. (2022). LiDAR Point Clouds Usage for Mapping the Vegetation Cover of the “Fryderyk” Mine Repository. Remote Sens., 15. 5. Zheng, X., He, X., Yang, X., Ma, H., Yu, Z., Ren, G., Li, J., Zhang, H., and Zhang, J. (2020). Terrain point cloud assisted GB-InSAR slope and pavement deformation differentiate method in an open-pit mine. Sensors, 20.
|
|