scholarly journals Registration of Terrestrial Laser Scanning Surveys Using Terrain-Invariant Regions for Measuring Exploitative Volumes over Open-Pit Mines

2019 ◽  
Vol 11 (6) ◽  
pp. 606 ◽  
Author(s):  
Zhihua Xu ◽  
Ershuai Xu ◽  
Lixin Wu ◽  
Shanjun Liu ◽  
Yachun Mao
Keyword(s):  
Laser Scanning ◽  
State Of The Art ◽  
Rigid Motion ◽  
Terrestrial Laser Scanning ◽  
Open Pit ◽  
Time Interval ◽  
Open Pit Mines ◽  
Registration Method ◽  
Art Methods ◽  
Invariant Regions

Terrestrial laser scanning (TLS) techniques have been widely used in open-pit mine applications. It is a crucial task to measure the exploitative volume of open-pit mines, within a specific time interval. One major challenge is posed, however, when conducting accurate registrations for temporal TLS surveys in continuously changing areas, created by excavation activities. In this paper, we propose a coarse-to-fine registration method, based on terrain-invariant regions (TIR), for temporal TLS surveys. More specifically, an approximate four-point congruent set (4PCS) of temporal TLS surveys is first identified, based on affine invariant rules. Second, a set of correspondences among temporal TLS surveys were collected by matching multi-scale sparse features of the 3D neighbors, centered at the approximate 4PCS. Third, the correspondences were used to estimate a rigid motion between the overlapping TLS surveys for the coarse registration, according to which the initial TIR from temporal TLS surveys were identified. Finally, the rigid motion between temporal TLS was iteratively optimized, based on the point clouds, only from the TIR. Based on the fine-level registered TLS surveys, Digital Elevation Models (DEMs) can be generated to calculate the exploitative volume, through a DEM differential. We applied the proposed method to two open-pit mines in China, and also compared our method with five state-of-the-art methods for registering temporal TLS surveys. Experimental results indicated that the proposed method achieved a higher registration accuracy than the state-of-the-art methods. Based on the registered result, our method achieved a 98.03% overall accuracy for measuring the exploitative volume, compared to in-situ measurement.

scholarly journals Slope stability monitoring in open pit mines using 3D terrestrial laser scanning

2018 ◽  
Vol 66 ◽  
pp. 01020
Author(s):  
Miroslawa Bazarnik
Keyword(s):  
Slope Stability ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Open Pit ◽  
Monitoring Methods ◽  
Rock Falls ◽  
Mining Operations ◽  
Open Pit Mines ◽  
Scanning Method ◽  
Stability Monitoring

In open pit mines the aspect of preventing and forecasting the threat of landslides and rock falls is crucial issue because of the significant consequences that instabilities may have. Systematic slope stability monitoring is necessary to ensure safe and continuous mining operations. The development of innovative technologies, such as 3D laser scanning, opens up new possibilities, especially in the case of large and hard-to-reach areas, such as open pit mines. Terrestrial laser scanners (TLS) provide fast, efficient, detailed, and accurate three-dimensional data. The article discusses the use of 3D terrestrial laser scanning method to monitor slope displacements and landslides in open pit mines. The first part of the article discusses the risk scale of gravitational displacement on the slopes, on examples of Polish open pit mines, and introduces the most common slope monitoring methods. Then, the principles of 3D terrestrial laser scanning were defined, and some examples of TLS applications in the open pit mines were presented.

scholarly journals An Improved Point-to-Plane Registration Method for Terrestrial Laser Scanning Data

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 48062-48073 ◽  
Author(s):  
Wuyong Tao ◽  
Xianghong Hua ◽  
Kegen Yu ◽  
Xiaoxing He ◽  
Xijiang Chen
Keyword(s):  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Registration Method

scholarly journals Accurate Virtual Trial Assembly Method of Prefabricated Steel Components Using Terrestrial Laser Scanning

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yin Zhou ◽  
Daguang Han ◽  
Kaixin Hu ◽  
Guocheng Qin ◽  
Zhongfu Xiang ◽  
...  
Keyword(s):  
Finite Element ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Simulation Method ◽  
Mean Stress ◽  
Registration Accuracy ◽  
Element Analysis ◽  
Registration Method ◽  
Assembly Method

The comprehensive utilization of prefabricated components (PCs) is one of the features of industrial construction. Trial assembly is imperative for PCs used in high-rise buildings and large bridges. Virtual trial assembly (VTA) is a preassembly process for PCs in a virtual environment that can avoid the time-consuming and economic challenges in physical trial assembly. In this study, a general framework for VTA that is performed between a point cloud, a building information model (BIM), and the finite element method is proposed. In obtaining point clouds via terrestrial laser scanning, the registration accuracy of point clouds is the key to building an accurate digital model of PCs. Accordingly, an accurate registration method based on triangular pyramid markers is proposed. This method can enable the general registration accuracy of point clouds to reach the submillimeter scale. Two algorithms for curved members and bolt holes are developed for PCs with bolt assembly to reconstruct a precise BIM that can be used directly in finite element analysis. Furthermore, an efficient simulation method for accurately predicting the elastic deformation and initial stress caused by forced assembly is proposed and verified. The proposed VTA method is verified on a reduced-scale steel pipe arch bridge. Experimental results show that the geometric prediction deviation of VTA is less than 1/1800 of the experimental bridge span, and the mean stress predicted via VTA is 90% of the measured mean stress. In general, this research may help improve the industrialization level of building construction.

scholarly journals Application of the Terrestrial Laser Scanning in Slope Deformation Monitoring: Taking a Highway Slope as an Example

2020 ◽  
Vol 10 (8) ◽  
pp. 2808 ◽  
Author(s):  
Chao Yin ◽  
Haoran Li ◽  
Zhinan Hu ◽  
Ying Li
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Deformation Monitoring ◽  
Slope Deformation ◽  
Model Parameters ◽  
Feature Points ◽  
Registration Method ◽  
Icp Algorithm ◽  
Fine Registration

Slope deformation monitoring is the prerequisite for disaster risk assessment and engineering control. Terrestrial laser scanning (TLS) is highly applicable to this field. Coarse registration method of point cloud based on scale-invariant feature transform (SIFT) feature points and fine registration method based on the k-dimensional tree (K-D tree) improved iterative closest point (ICP) algorithm were proposed. The results show that they were superior to other algorithms (such as speeded-up robust features (SURF) feature points, Harris feature points, and Levenberg-Marquardt (LM) improved ICP algorithm) when taking the Stanford Bunny as an example, and had high applicability in coarse and fine registration. In order to integrate the advantages of point measurement and surface measurement, an improved point cloud comparison method was proposed and the optimal model parameters were determined through model tests. A case study was conducted on the left side of the K146 + 150 point at S236 Boshan section, Shandong Province, and research results show that from 14 August 2018 and 9 November 2019, the overall deformation of the slope was small with a maximum value of 0.183 m, and the slope will continue to maintain a stable state without special inducing factors such as earthquake, heavy rainfall and artificial excavation.

scholarly journals An Improvement in the Identification of the Centres of Checkerboard Targets in Point Clouds Using Terrestrial Laser Scanning

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 938 ◽  
Author(s):  
Anna Fryskowska
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Target Identification ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Reference Points ◽  
Global Coordinate System ◽  
Registration Method ◽  
Cloud Parameters ◽  
The Difference

Measurement using terrestrial laser scanning is performed at several stations to measure an entire object. In order to obtain a complete and uniform point cloud, it is necessary to register each and every scan in one local or global coordinate system. One registration method is based on reference points—in this case, checkerboard targets. The aim of this research was to analyse the accuracy of checkerboard target identification and propose an algorithm to improve the accuracy of target centre identification, particularly for low-resolution and low-quality point clouds. The proposed solution is based on the geometric determination of the target centre. This work presents an outline of a new approach, designed by the author, to discuss the influence of the point cloud parameters on the process of checkerboard centre identification and to propose an improvement in target centre identification. The validation of the proposed solutions reveals that the difference between the typical automatic target identification and the proposed method amounts to a maximum of 6 mm for scans of different qualities. The proposed method may serve as an alternative to, or supplement for, checkerboard identification, particularly when the quality of these scans is not sufficient for automatic algorithms.

Robust Markerless Registration of Point Clouds for Terrestrial Laser Scanning-Based Measurement of Bulk Grains Stockpiled in Storehouses

2021 ◽  
Vol 37 (6) ◽  
pp. 1073-1087
Author(s):  
Xingbo Hu ◽  
Leidong Yang ◽  
Fangming Wu ◽  
Yinghong Tian
Keyword(s):  
Computational Efficiency ◽  
Point Cloud ◽  
Laser Scanning ◽  
Ordered Set ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
List Type ◽  
Registration Method ◽  
Geometrical Features ◽  
Symmetric Structures

HighlightsFully automated registration free from artificial markers for multi-scan point clouds aimed for TLS-based measurement of bulk grains in large storehouses.The geometric structure of the large grain storehouse is explored to derive geometrical features as the structurally semantic information for scene understanding.The geometrical features are modeled as a small ordered set and correspondences are established by performing trials for all possible matching pairs of two sets extracted from two different scans.Significant improvements have been achieved in registration accuracy, computational efficiency, and robustness against scenes with symmetric structures as well as the immunity to noises and varying point density.Abstract. Point clouds collected by terrestrial laser scanning (TLS) in the application of bulk grain measurement and quantification contain a vast amount of data, relatively low-textured surfaces and highly symmetric structures. All of these challenges make it a difficult task to automatically register multiple scans from different viewpoints needed to fully cover a large-scale scene. To address the challenges, this article presents a robust automatic marker-free registration method dedicated for multi-scan TLS point cloud data captured in large grain storehouses. The framework of the dedicated method follows the common procedure to split the entire registration into coarse alignment and fine registration, and uses the iterative closest point (ICP) algorithm for the latter. The main contribution of the proposed dedicated method is an efficient way to find a global coarse alignment that is robust across individual scans in a TLS-based bulk grain measurement project. To tackle the correspondence problem, which is at the core of a registration task, the geometric information inherent in grain storehouses is explored in the stage of global coarse alignment. The derived semantic feature points are modeled as a small ordered set and reliable correspondences are established by performing trials for all possible matching pairs of two sets extracted from two different scans. Experimental results show the dedicated method outperforms the existing generic markless registration approaches in terms of accuracy, robustness and computational efficiency. With robustness, efficiency and accuracy, the proposed markless point cloud registration method dedicated for bulk grain measurement can cover a gap between the TLS technology and various granary field applications. Especially, its applicability to the dominant storage structure in Chinese huge grain reserve system implies remarkable efficiency improvements and will facilitate the application of TLS-based measurement in the national grain inventory of China. Keywords: Bulk grain measurement, Feature extraction, Grain storehouse, Markerless registration, Point cloud, Terrestrial laser scanning.

A Structure-Based Registration Method for Terrestrial Laser Scanning Data

2015 ◽  
Vol 734 ◽  
pp. 608-616
Author(s):  
Jun Cheng ◽  
Ming Cheng ◽  
Yan Bin Lin ◽  
Cheng Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Optimization Problem ◽  
Terrestrial Laser Scanning ◽  
Computation Method ◽  
Registration Method ◽  
Global Registration ◽  
Novel Structure ◽  
Scene Structure ◽  
Evolution Computation

This paper presents a novel structure-based registration method for terrestrial laser scanning (TLS) data. The line support region (LSR), which fits the 3D line segment, is adopted to describe the scene structure and reduce geometric complexity. Then we employ an evolution computation method to solve the optimization problem of global registration. Our method can be further enhanced by iterative closest points (ICP) or other local registration methods. We demonstrate the robustness of our algorithm on several point cloud sets with varying extent of overlap and degree of noise.

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