A Coarse-to-Fine Approach for Rock Bolt Detection From 3D Point Clouds

Saydam, Sarp; Liu, Boge; Li, Binghao; Zhang, Wenjie; Singh, S. K.; Raval, Simit

doi:10.1109/access.2021.3120207

Cited by 8 publications

(3 citation statements)

References 31 publications

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“…The BIM applications show the insights into near realtime modelling, data-driven modelling, and analytics fusion. Based on this, 3D Simultaneous Localisation and Mapping (SLAM) and related point cloud data model development are emerging and attracting mining managers [150]. Ren et al [55], [56] studied utilising high precise SLAM and UAV devices to model underground workings.…”

Section: B Parametric Modelling and Information Sharingmentioning

confidence: 99%

Exploring the Fusion Potentials of Data Visualization and Data Analytics in the Process of Mining Digitalization

et al. 2023

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Mining digitalisation have been receiving significant attention due to the utilisation of advanced technologies, such as IoT, automation, and sensing. However, maximising the potential value of collected data in the mining industry remains a challenge. Therefore, this paper aims to review timely concern topics to facilitate the fusion implementation in mining engineering. Specifically, this review covers recent popular topics, such as, data visualisation, data management, data analytics, data fusion, visual analytics, and mining digital twin construction. In this paper, we aim to draw a comprehensive picture about the fusion of data visualisation and analytics in the big data context, by examining the recent academic research related to these topics. Therefore, this paper reviews the visualisation domain by conventional classification, including scientific visualisation, information visualisation, and visual analytics, associated with the analysis of current digital twin development. Next, according to the challenges and issues related to visualisation development, this paper reviews the data management and data analytics domains as well. Incorporating with the fusion concept, machine learning-oriented fusion applications and potential scenarios in the mining industry have been discussed. In addition, based on the observation across various domains, this paper presents challenges and future potentials of data fusion in mining.INDEX TERMS mining digitalisation, data management, data analytics, data visualisation, and data fusion I. INTRODUCTION

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Section: B Parametric Modelling and Information Sharingmentioning

confidence: 99%

Exploring the Fusion Potentials of Data Visualization and Data Analytics in the Process of Mining Digitalization

et al. 2023

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“…realized the construction of a full 3D model of a canal tunnel in France by combining terrestrial laser and sonar scans collected from static acquisitions. Saydam et al, (2021) presents a practical algorithm, CFBolt, to detect rock bolts from a 3D laser scanned point cloud and CFBolt was tested for detecting rock bolts from LiDAR scan data collected from Sydney's civil tunnelling project site.…”

Section: Introductionmentioning

confidence: 99%

Automatic Crack Detection and Quantification for Tunnel Lining Surface from 3D Terrestrial LiDAR Data

Zhou

Gao²,

Wu³

2022

JER is an international, peer-reviewed journal that publishes f

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In the process of tunnel construction and operation, the traditional method of crack inspection in tunnel operation is through manual photographing and field recording which is not only labor-intensive with lower data quality but also hard to obtain the important geometric information of the cracks. With the development of terrestrial laser scanning technology, Three-dimensional point cloud collection from a tunnel becomes feasible and affordable. In this paper, we propose an automatic crack detection method to automatically identify the cracks from the 3D point cloud of a tunnel and also extract the corresponding dimension information of the cracks. Through a field experiment with a tunnel project, the feasibility of the proposed method was validated, and the experiment results showed that the proposed method could detect the severe crack diseases with a width of more than 0.6 mm with the relative errors of the width and the length within 10%.

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“…Gallwey et al [ 24 ] combined machine learning with domain attribute-based feature descriptors and constructed a 65-dimensional feature vector for each point, and used the density-based spatial clustering of applications with noise (DBSCAN) algorithm to divide the results into candidate bolt objects. Saydam et al [ 25 ] presented a practical algorithm, CFBolt, to detect rock bolts from a 3D laser-scanned point cloud. It computed a single-scale proportion of variance (POV) for each point as the local point descriptor and filtered out near 95% of the not-bolt points with a simple but effective classifier, Linear Discriminant Analysis (LDA).…”

Section: Introductionmentioning

confidence: 99%

A Geometric-Feature-Based Method for Automatic Extraction of Anchor Rod Points from Dense Point Cloud

Liu

Yue

Zheng

et al. 2022

Sensors

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As the technology of high-precision 3D laser scanning becomes increasingly prevalent in the fields of hydraulic building modeling and deformation monitoring, the quality of point clouds plays an increasingly crucial role in data processing. This paper investigates an automatic extraction method of anchor rod points based on geometric features, which focuses on the influence of anchor rod points and mixed pixels in the data of an underground powerhouse of a pumped storage power station on modeling and deformation monitoring during the construction period. This workflow consists of two steps that can automatically extract anchor rod points from high-density point cloud data. Triangular mesh features in the local neighborhood and the parameters of the anchor rods are used to locate the anchor rod in downsampled data, and curvature features are used to extract anchor rod points precisely. The experiment of extracting anchor rods shows that the accuracy of this method of initial identification is 97.2%. Furthermore, precise extraction based on curvature curve fitting is applicable. This method can accurately separate the three types of anchor rods from the dense point cloud on the rough surface of a cavern roof; the false-extraction rate of anchor rod points is about 0.11% to 5.09%. This method can provide high-quality and dependable data sources for the precise registration, modeling and deformation analysis of point clouds in a construction cavern.

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A Coarse-to-Fine Approach for Rock Bolt Detection From 3D Point Clouds

Cited by 8 publications

References 31 publications

Exploring the Fusion Potentials of Data Visualization and Data Analytics in the Process of Mining Digitalization

Exploring the Fusion Potentials of Data Visualization and Data Analytics in the Process of Mining Digitalization

Automatic Crack Detection and Quantification for Tunnel Lining Surface from 3D Terrestrial LiDAR Data

A Geometric-Feature-Based Method for Automatic Extraction of Anchor Rod Points from Dense Point Cloud

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