An Accurate TLS and UAV Image Point Clouds Registration Method for Deformation Detection of Chaotic Hillside Areas

Zang, Yufu; Wang, Jinhu; Li, Jianping; Guan, Haiyan

doi:10.3390/rs11060647

Cited by 36 publications

(27 citation statements)

References 54 publications

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“…The detection of geometric deformation is realized by TLS which rapidly capture high precision 3D point clouds of hillside areas [32]. It is proposed to use a novel registration algorithm in the deformation detection which registers TLS stations to the unmanned aerial vehicle dense image points accurately.…”

Section: Geometric Information Acquisition From Sensorsmentioning

confidence: 99%

Finite Element Analysis based on A Parametric Model by Approximating Point Clouds

Neumann

2020

Remote Sensing

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Simplified models are widely applied in finite element computations regarding mechanical and structural problems. However, the simplified model sometimes causes many deviations in the finite element analysis (FEA) of structures, especially in the non-designed structures which have undergone unknowable deformation features. Hence, a novel FEA methodology based on the parametric model by approximating three-dimensional (3D) feature data is proposed to solve this problem in the present manuscript. Many significant and effective technologies have been developed to detect 3D feature information accurately, e.g., terrestrial laser scanning (TLS), digital photogrammetry, and radar technology. In this manuscript, the parametric FEA model combines 3D point clouds from TLS and the parametric surface approximation method to generate 3D surfaces and models accurately. TLS is a popular measurement method for reliable 3D point clouds acquisition and monitoring deformations of structures with high accuracy and precision. The B-spline method is applied to approximate the measured point clouds data automatically and generate a parametric description of the structure accurately. The final target is to reduce the effects of the model description and deviations of the FEA. Both static and dynamic computations regarding a composite structure are carried out by comparing the parametric and general simplified models. The comparison of the deformation and equivalent stress of future behaviors are reflected by different models. Results indicate that the parametric model based on the TLS data is superior in the finite element computation. Therefore, it is of great significance to apply the parametric model in the FEA to compute and predict the future behavior of the structures with unknowable deformations in engineering accurately.

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Section: Geometric Information Acquisition From Sensorsmentioning

confidence: 99%

Finite Element Analysis based on A Parametric Model by Approximating Point Clouds

Neumann

2020

Remote Sensing

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“…In fact, the correct co‐registration of photogrammetric outputs (such as point clouds) is a delicate step to carry out in order to minimise the distance between corresponding points and reduce the introduction of errors (Micheletti et al, 2015; Scaioni et al, 2015). Depending on the application, a range of strategies are generally adopted to minimise errors in the similarity transformation (rotation, translation and scaling) applied to the multitemporal 3D models (for example, Zang et al, 2019; Cucchiaro et al, 2020). A detailed description of registration approaches is provided by Al‐Rawabdeh et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Automated Registration of SfM‐MVS Multitemporal Datasets Using Terrestrial and Oblique Aerial Images

Parente

Chandler

Dixon

2021

The Photogrammetric Record

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Accurate alignment of 3D models is critical for valid change‐detection analysis from multitemporal photogrammetric datasets. This paper assesses an automated registration strategy which uses the scale‐invariant feature transform (SIFT) algorithm implemented in modern photogrammetric software. This registration solution, also known as “Time‐SIFT”, was tested at two study sites featuring vertical surfaces, including a sea cliff (~500 m2) and a quarry face (~50 000 m2). Tests demonstrated that the investigated registration strategy can achieve accurate alignments between multitemporal point clouds even when using multisource and multi‐perspective data, captured across widely varying spatial and temporal scales and under a range of weather and illumination conditions. The combination of the Time‐SIFT approach with an ICP algorithm produced moderate improvements in the alignment. Furthermore, the use of an innovative direct georeferencing technique, which used the tracking feature of a robotic total station, allowed for accurate georectification of 3D models.

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“…Over the last decade, unmanned aerial vehicles (UAVs) have been utilized in a wide range of military and civilian applications, including surveillance [1,2], monitoring [3,4], imaging [5,6], and reconnaissance [7]. UAVs boast many advantages, including remarkable flexibility, low energy consumption, high efficiency and the capacity for real-time monitoring.…”

Section: Introductionmentioning

confidence: 99%

Collision Detection for UAVs Based on GeoSOT-3D Grids

Zhai

Tong

Miao

et al. 2019

IJGI

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The increasing number of unmanned aerial vehicles (UAVs) has led to challenges related to solving the collision problem to ensure air traffic safety. The traditional approaches employed for collision detection suffer from two main drawbacks: first, the computational burden of a pairwise calculation increases exponentially with an increasing number of spatial entities; second, existing grid-based approaches are unsuitable for complicated scenarios with a large number of objects moving at high speeds. In the proposed model, we first identified UAVs and other spatial objects with GeoSOT-3D grids. Second, the nonrelational spatial database was initialized with a multitable strategy, and spatiotemporal data were inserted with the GeoSOT-3D grid codes as the primary key. Third, the collision detection procedure was transformed from a pairwise calculation to a multilevel query. Four simulation experiments were conducted to verify the feasibility and efficiency of the proposed collision detection model for UAVs in different environments. The results also indicated that 64 m GeoSOT-3D grids are the most suitable basic grid size, and the reduction in the time consumption compared with traditional methods reached approximately 50–80% in different scenarios.

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An Accurate TLS and UAV Image Point Clouds Registration Method for Deformation Detection of Chaotic Hillside Areas

Cited by 36 publications

References 54 publications

Finite Element Analysis based on A Parametric Model by Approximating Point Clouds

Finite Element Analysis based on A Parametric Model by Approximating Point Clouds

Automated Registration of SfM‐MVS Multitemporal Datasets Using Terrestrial and Oblique Aerial Images

Collision Detection for UAVs Based on GeoSOT-3D Grids

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