Benchmark of multi-view Terrestrial Laser Scanning Point Cloud data registration algorithms

Bedkowski, Janusz

doi:10.1016/j.measurement.2023.113199

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…The third strategy is descriptor-based. In this method, descriptors of both the source and target point clouds are computed, and point pairs are formed from points with identical descriptor features for registration [26,33,34].…”

Section: Related Work 21 Registration Methodsmentioning

confidence: 99%

“…Typically, a project is large, and it is unlikely that a single scan can cover all the data required. Additionally, multiple devices may be used together, each performing its own point cloud data collection, with the data existing in an independent coordinate system [25,26]. To generate a comprehensive point cloud model, it is necessary to accurately synthesize the data from different parts of the project into a common coordinate system using appropriate point cloud registration methods.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Impact of Different Registration Methods and Noise Removal on the Registration Quality of Point Cloud Models in the Built Environment: A Case Study on Dickabrma Bridge

Zhu,

Rowlinson,

Chen

et al. 2023

Buildings

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Point cloud models are prevalently utilized in the architectural and civil engineering sectors. The registration of point clouds can invariably introduce registration errors, adversely impacting the accuracy of point cloud models. While the domain of computer vision has delved profoundly into point cloud registration, limited research in the construction domain has explored these registration algorithms in the built environment, despite their inception in the field of computer vision. The primary objective of this study is to investigate the impact of mainstream point cloud registration algorithms—originally introduced in the computer vision domain—on point cloud models, specifically within the context of bridge engineering as a category of civil engineering data. Concurrently, this study examines the influence of noise removal on varying point cloud registration algorithms. Our research quantifies potential variables for registration quality based on two metrics: registration error (RE) and time consumption (TC). Statistical methods were employed for significance analysis and value engineering assessment. The experimental outcomes indicate that the GRICP algorithm exhibits the highest precision, with RE values of 3.02 mm and 2.79 mm under non-noise removal and noise removal conditions, respectively. The most efficient algorithm is PLICP, yielding TC values of 3.86 min and 2.70 min under the aforementioned conditions. The algorithm with the optimal cost-benefit ratio is CICP, presenting value scores of 3.57 and 4.26 for non-noise removal and noise removal conditions, respectively. Under noise removal conditions, a majority of point cloud algorithms witnessed a notable enhancement in registration accuracy and a decrease in time consumption. Specifically, the POICP algorithm experienced a 32% reduction in RE and a 34% decline in TC after noise removal. Similarly, PLICP observed a 34% and 30% reduction in RE and TC, respectively. KICP showcased a decline of 23% in RE and 28% in TC, CICP manifested a 27% and 31% drop in RE and TC, respectively, GRICP observed an 8% reduction in RE and a 40% decline in TC, and for FGRICP, RE and TC decreased by 8% and 52%, respectively, subsequent to noise removal.

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Section: Related Work 21 Registration Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring the Impact of Different Registration Methods and Noise Removal on the Registration Quality of Point Cloud Models in the Built Environment: A Case Study on Dickabrma Bridge

Zhu,

Rowlinson,

Chen

et al. 2023

Buildings

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“…Coarse registration defines initial parameters for transformation between two point clouds through feature-based methods (points, lines, surfaces, or combinations). In contrast, fine registration aims for maximal overlap using iterative approximation techniques [13,28,29].…”

Section: Second Approachmentioning

confidence: 99%

A Multimodal Fusion System for Object Identification in Point Clouds with Density and Coverage Differences

Quintero Bernal,

Kern,

Urrea

2024

Processes

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Data fusion, which involves integrating information from multiple sources to achieve a specific objective, is an essential area of contemporary scientific research. This article presents a multimodal fusion system for object identification in point clouds in a controlled environment. Several stages were implemented, including downsampling and denoising techniques, to prepare the data before fusion. Two denoising approaches were tested and compared: one based on neighborhood technique and the other using a median filter for each “x”, “y”, and “z” coordinate of each point. The downsampling techniques included Random, Grid Average, and Nonuniform Grid Sample. To achieve precise alignment of sensor data in a common coordinate system, registration techniques such as Iterative Closest Point (ICP), Coherent Point Drift (CPD), and Normal Distribution Transform (NDT) were employed. Despite facing limitations, variations in density, and differences in coverage among the point clouds generated by the sensors, the system successfully achieved an integrated and coherent representation of objects in the controlled environment. This accomplishment establishes a robust foundation for future research in the field of point cloud data fusion.

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Flexible registration strategy for optimizing machining allowance of thin-walled parts

Lin,

Zhao,

2024

Measurement

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Benchmark of multi-view Terrestrial Laser Scanning Point Cloud data registration algorithms

Cited by 6 publications

References 31 publications

Exploring the Impact of Different Registration Methods and Noise Removal on the Registration Quality of Point Cloud Models in the Built Environment: A Case Study on Dickabrma Bridge

Exploring the Impact of Different Registration Methods and Noise Removal on the Registration Quality of Point Cloud Models in the Built Environment: A Case Study on Dickabrma Bridge

A Multimodal Fusion System for Object Identification in Point Clouds with Density and Coverage Differences

Flexible registration strategy for optimizing machining allowance of thin-walled parts

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