3D Digital protection and representation of burial ruins based on LiDAR and UAV survey

Guo, Ming; Fu, Zexin; Pan, Deng; Zhou, Yuquan; Huang, Mei‐Hua; Guo, Kecai

doi:10.1177/00202940221110949

Cited by 6 publications

(8 citation statements)

References 13 publications

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“…The starting point for any intervention on ruins is certainly a historical analysis followed by a survey that allows us to know and correctly determine and interpret their shape, spatial organisation, materials, and degradation [14]. Articles dealing with the documentation of ruined buildings can be found in the scientific literature [14][15][16][17][18][19][20][21], especially when the buildings are of architectural significance.…”

Section: Digital Documentation Of Ruinsmentioning

confidence: 99%

Drones and Real-Time Kinematic Base Station Integration for Documenting Inaccessible Ruins: A Case Study Approach

Treccani,

Adami,

Fregonese

2024

Drones

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Ruins, marked by decay and abandonment, present challenges for digital documentation due to their varied conditions and remote locations. Surveying inaccessible ruins demands innovative approaches for safety and accuracy. Drones with high-resolution cameras enable the detailed aerial inspection and imaging of these inaccessible areas. This study investigated how surveying technologies, particularly Unmanned Aerial Vehicles (UAVs), are used to document inaccessible ruins. Integration with Real-Time Kinematic (RTK) technologies allows direct georeferencing in photogrammetric processing. A case study of the Castle of Terracorpo in Italy was used to demonstrate UAV-only surveying feasibility in inaccessible environments, testing two different scenarios. The first scenario involved the use of a DJI Matrice 300 RTK coupled with the D-RTK2 base station to survey the Castle; both direct and indirect georeferencing were exploited and compared through the photogrammetric process. This first scenario confirmed that this approach can lead to a centimetre-level accuracy (about three times the GSD value for indirect georeferencing and seven times the GSD value for direct georeferencing exploting RTK). The second scenario testing the integration of data from drones at varying resolutions enabled the comprehensive coverage of ruinous structures. In this case, the photogrammetric survey performed with the dji Mavic 3 Cine drone (indirect georeferencing) was integrated with the photogrammetric survey performed with the dji Matrice 300 RTK drone (direct georeferencing). This scenario showed that GCPs extracted from a direct georeferencing photogrammetric survey could be successfully used to georeference and integrate other drone data. However, challenges persist in surveying underground or enclosed spaces that are inaccessible to UAVs. Future research will explore integrating robotic LiDAR survey systems and advanced data fusion techniques to enhance documentation.

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Section: Digital Documentation Of Ruinsmentioning

confidence: 99%

Drones and Real-Time Kinematic Base Station Integration for Documenting Inaccessible Ruins: A Case Study Approach

Treccani,

Adami,

Fregonese

2024

Drones

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“…(2) Different from the monogeneity of feature point information in literature, [33][34][35][36][37] the algorithm determines feature contour points [50][51][52]…”

Section: Contributionmentioning

confidence: 99%

“…Common key point extraction is based on curvature change, 30 covariance matrix, 31 Gaussian difference, and scale space. 32 Such as Intrinsic Shape Signatur (ISS), 33 Normal Aligned Radial Feature (NARF), 34 Scale-invariant feature transform (SIFT), 35,36 3D Harris, 37 and other algorithms. However, the feature point algorithm with single information has some problems, such as low robustness, easy to be affected by noise, and large time complexity.…”

Section: Introductionmentioning

confidence: 99%

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Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction

Zhao,

Zhang,

et al. 2023

Measurement and Control

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For iterative closest point (ICP) algorithm, the initial position and the number of iterations are needed in registration. At the same time, the ICP algorithm is easy to fall into local convergence and convergence speed is slow. By constructing K-D tree to search neighborhood points and artificially set threshold, plane fitting is carried out, the on-time point cloud to be deployed is separated from the complex background, and statistical analysis is used to calculate the distance between the point cloud and the neighborhood point to quickly remove the invalid point cloud. The surface equation is set to calculate the tangent plane of point cloud normal vector and each normal vector, and the local coordinate system is constructed. The angle between adjacent vectors and the local coordinate system is calculated to determine the feature point set of edge contour. According to the covariance matrix of the feature points set, the principal feature component is obtained, the principal axis direction of the two sets of point clouds is found, and the rotation matrix and the displacement vector are obtained. Finally, GICP precise registration of point cloud is carried out according to initial pose parameters and rigid body transformation matrix obtained by maximum likelihood estimation method. The results show that the optimized algorithm can effectively avoid local convergence. Compared with the traditional ICP algorithm, when the algorithm achieves the same registration accuracy in the public dataset experiment, the registration speed is on average 44.82% faster and the overlap rate is on average 15.26% higher. In the real dataset experiment, the registration speed is on average 59.04% faster, the registration accuracy is on average 30.24% higher and the overlap rate is on average 10.61% higher. This shows that the optimization algorithm is superior to the traditional ICP algorithm in registration accuracy and convergence speed.

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“…[22] Photogrammetry technology generates 3D points based on stereo matching of image pairs, [23,24] which are derived through structure from motion (SfM) or multi-view stereo (MVS). [25] LiDAR measures the angle and distance of reflection points by emitting a large number of laser pulses per second. [26] According to the different carrying platforms, LiDAR can be further divided into airborne laser scanning (ALS), [27,28] mobile laser scanning (MLS), [29,30] and terrestrial laser scanning (TLS).…”

Section: Introductionmentioning

confidence: 99%

3D Change Detection Method for Exterior Wall of LNG Storage Tank Supported by Multi‐Source Spatial Data

Gao,

Guo,

Wang

et al. 2024

Advcd Theory and Sims

Self Cite

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Change detection methods for measuring deformation of the LNG storage tank's exterior wall cannot meet the requirements for extensive and comprehensive deformation analysis. The method proposed in this paper uses multi‐source spatial data to detect the deformation of the LNG storage tank's exterior wall. Exterior wall data collected simultaneously by terrestrial and airborne laser scanners, as well as close‐range and oblique photogrammetry, are pre‐processed separately and then registered to the global point clouds. The deformation state of exterior wall is represented by a thorough assessment approach that takes into account four change indices. The results suggest that the registration precision of global point clouds is 6 mm. The global ovality of the LNG storage tank is 0.053%, with a tilt change of 2.58° and a deviation of 27 mm in the direction of the azimuth of 321.98°, an average value of 0.07 mm for depression and protrusion deformation, and a surface flatness of 21.3 mm. In this case, the flatness is greater than the variation threshold of the standard specification, indicating that the tank may have structural uneven settlement or other deformation problems. The attraction of the proposed method resides in its capacity to efficiently and accurately obtain spatial positioning and color information of the LNG storage tank's exterior wall, thereby providing a robust foundation for precise judgments regarding the deformation state.

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3D Digital protection and representation of burial ruins based on LiDAR and UAV survey

Cited by 6 publications

References 13 publications

Drones and Real-Time Kinematic Base Station Integration for Documenting Inaccessible Ruins: A Case Study Approach

Drones and Real-Time Kinematic Base Station Integration for Documenting Inaccessible Ruins: A Case Study Approach

Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction

3D Change Detection Method for Exterior Wall of LNG Storage Tank Supported by Multi‐Source Spatial Data

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