Terrain Point Cloud Assisted GB-InSAR Slope and Pavement Deformation Differentiate Method in an Open-Pit Mine

Zheng, Xiangtian; He, Xiufeng; Yang, Xiaolin; Ma, Haitao; Yu, Zhonghua; Ren, Guiwen; Li, Jiang; Zhang, Hao; Zhang, Jinsong

doi:10.3390/s20082337

Cited by 10 publications

(6 citation statements)

References 16 publications

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“…Accurate excavation quantity calculation is pivotal for enhancing mine production monitoring and cost management, representing a fundamental task in open-pit mine surveying and acceptance [3]. In recent years, advanced methods with high accuracy and speed, such as 3D laser scanning and UAV photogrammetry, have been extensively employed for measuring excavation or settlement volumes [4][5][6], landslide analysis [7], and calculating mining volume [8]. These methods facilitate the rapid construction of 3D point cloud models for large-scale irregular bodies, such as open-pit mines [9].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining

Meng,

Wang,

Cheng

et al. 2023

Remote Sensing

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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.

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Section: Introductionmentioning

confidence: 99%

Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining

Meng,

Wang,

Cheng

et al. 2023

Remote Sensing

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“…The current open-pit mine describes the spatial information of the open-pit mine and the mining status of the open pit through the step-feature line. In recent years, UAV 3D point-cloud-data acquisition and processing technology [ 1 , 2 , 3 ] has been widely used in the field of 3D modeling, and in the spatial analysis of open-pit mines after continuous and rapid development [ 4 , 5 , 6 , 7 ]. Although UAV point-cloud technology has made great contributions to mine automation, step-feature-line drawing is still mainly realized by manual visual interpretation.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Step-Feature Lines in Open-Pit Mines Based on UAV Point-Cloud Data

Mao

Wang

et al. 2022

Sensors

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Step-feature lines are one of the important geometrical elements for drawing the status quo maps of open-pit mines, and the efficient and accurate automatic extraction and updating of step-feature lines is of great significance for open-pit-mine stripping planning and analysis. In this study, an automatic extraction method of step-feature lines in an open-pit mine based on unmanned-aerial-vehicle (UAV) point-cloud data is proposed. The method is mainly used to solve the key problems, such as low accuracy, local-feature-line loss, and the discontinuity of the step-feature-line extraction method. The method first performs the regular raster resampling of the open-pit-mine cloud based on the MLS algorithm, then extracts the step-feature point set by detecting the elevation-gradient change in the resampled point cloud, further traces the step-feature control nodes by the seed-growth tracking algorithm, and finally generates smooth step-feature lines by fitting the space curve to the step-feature control nodes. The results show that the method effectively improves the accuracy of step-feature-line extraction and solves the problems of local-feature-line loss and discontinuity.

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“…At present, the processing technology of three-dimensional point cloud data is being applied more and more frequently in the mining field [1][2][3]. The national robotics engineering center of the United States can successfully draw a high-precision, three-dimensional map of underground roadways using the point cloud data obtained by a three-dimensional laser scanner and then propose an intelligent mining mode based on the three-dimensional map [4].…”

Section: Introductionmentioning

confidence: 99%

Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN

Zhao

Guo

et al. 2021

IJGI

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Point cloud data can accurately and intuitively reflect the spatial relationship between the coal wall and underground fully mechanized mining equipment. However, the indirect method of point cloud feature extraction based on deep neural networks will lose some of the spatial information of the point cloud, while the direct method will lose some of the local information of the point cloud. Therefore, we propose the use of dynamic graph convolution neural network (DGCNN) to extract the geometric features of the sphere in the point cloud of the fully mechanized mining face (FMMF) in order to obtain the position of the sphere (marker) in the point cloud of the FMMF, thus providing a direct basis for the subsequent transformation of the FMMF coordinates to the national geodetic coordinates with the sphere as the intermediate medium. Firstly, we completed the production of a diversity sphere point cloud (training set) and an FMMF point cloud (test set). Secondly, we further improved the DGCNN to enhance the effect of extracting the geometric features of the sphere in the FMMF. Finally, we compared the effect of the improved DGCNN with that of PointNet and PointNet++. The results show the correctness and feasibility of using DGCNN to extract the geometric features of point clouds in the FMMF and provide a new method for the feature extraction of point clouds in the FMMF. At the same time, the results provide a direct early guarantee for analyzing the point cloud data of the FMMF under the national geodetic coordinate system in the future. This can provide an effective basis for the straightening and inclining adjustment of scraper conveyors, and it is of great significance for the transparent, unmanned, and intelligent mining of the FMMF.

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Terrain Point Cloud Assisted GB-InSAR Slope and Pavement Deformation Differentiate Method in an Open-Pit Mine

Cited by 10 publications

References 16 publications

Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining

Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining

Extraction of Step-Feature Lines in Open-Pit Mines Based on UAV Point-Cloud Data

Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN

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