Alignment of 3D point cloud, CAD model, real-time camera view and partial point cloud for pipeline retrofitting application

Kumar, Gaurav; Patil, Ashok Kumar; Chai, Young Ho

doi:10.23919/elinfocom.2018.8330627

Cited by 6 publications

(7 citation statements)

References 9 publications

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“…In addition, infrastructural inspection and structural health-monitoring applications depend on information extracted from thermal images and LiDAR scans collected by UASs (Unmanned Aircraft System) for precise close-range inspection and trajectory control [14]. These utilizations are added to retrofitting applications that align 3D laser scans with camera videos for complex industrial modeling such as those in pipeline plants [15]; aerial cinematography that utilizes dynamic camera viewpoints for entertainment, sports, and security applications [16]; and synchronized human-robot interaction applications such as the prediction of pedestrian attitude on roads for potential urban automated driving [17].…”

Section: Introductionmentioning

confidence: 99%

Fusion of Airborne LiDAR Point Clouds and Aerial Images for Heterogeneous Land-Use Urban Mapping

2021

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The World Health Organization has reported that the number of worldwide urban residents is expected to reach 70% of the total world population by 2050. In the face of challenges brought about by the demographic transition, there is an urgent need to improve the accuracy of urban land-use mappings to more efficiently inform about urban planning processes. Decision-makers rely on accurate urban mappings to properly assess current plans and to develop new ones. This study investigates the effects of including conventional spectral signatures acquired by different sensors on the classification of airborne LiDAR (Light Detection and Ranging) point clouds using multiple feature spaces. The proposed method applied three machine learning algorithms—ML (Maximum Likelihood), SVM (Support Vector Machines), and MLP (Multilayer Perceptron Neural Network)—to classify LiDAR point clouds of a residential urban area after being geo-registered to aerial photos. The overall classification accuracy passed 97%, with height as the only geometric feature in the classifying space. Misclassifications occurred among different classes due to independent acquisition of aerial and LiDAR data as well as shadow and orthorectification problems from aerial images. Nevertheless, the outcomes are promising as they surpassed those achieved with large geometric feature spaces and are encouraging since the approach is computationally reasonable and integrates radiometric properties from affordable sensors.

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Section: Introductionmentioning

confidence: 99%

Fusion of Airborne LiDAR Point Clouds and Aerial Images for Heterogeneous Land-Use Urban Mapping

2021

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“…However, complex analysis in manufacturing operations using point clouds implies certain challenges, which mainly belong to the heavy industry (Kumar et al, 2018;Moon et al, 2019). Machining manufacturing thrives for complex modeling in its operations due to the need for high-performance computations, which are resource hungry and as a result, their efficiency is more than a need (He et al, 2012).…”

Section: Related Workmentioning

confidence: 99%

Machining tool identification utilizing temporal 3D point clouds

et al. 2023

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The manufacturing domain is regarded as one of the most important engineering areas. Recently, smart manufacturing merges the use of sensors, intelligent controls, and software to manage each stage in the manufacturing lifecycle. Additionally, the increasing use of point clouds to model real products and machining tools in a virtual space facilitates the more accurate monitoring of the end-to-end production lifecycle. Thus, the conjunction of both, intelligent methods and more accurate 3D models allows the prediction of uncertainties and anomalies in the manufacturing process as well as reduces the final production costs. However, the high complexity of the geometrical structures defined by point clouds and the high accuracy required by the Quality Assurance/Quality control parameters during the process, pave the way for continuous improvements in smart manufacturing methods. This paper addresses a comprehensive analysis of machining tool identification utilizing temporal point cloud data. Specifically, we deal with the identification of machining tools from temporal 3D point clouds. To do that, we propose a process to construct and train intelligent models utilizing such data. Moreover, in our case study, we provide the research community with two labeled temporal 3D point cloud datasets, and we experiment with the pioneering PointNet neural network and three of its variants demonstrating an accuracy of 95% in the identification of the utilized machining tools in a machining process. Finally, we provide a prototype end-to-end intelligent system of machining tool identification.

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“…Aligning the captured point clouds with their CAD models is often done using the standard ICP method in the literature. [4,[24][25][26]. However, this typically involves using a (fully) scanned template model [24,26,27], instead of sampling from a CAD model.…”

Section: Introductionmentioning

confidence: 99%

“…[4,[24][25][26]. However, this typically involves using a (fully) scanned template model [24,26,27], instead of sampling from a CAD model. While popular, ICP's performance is sensitive to the initial pose of both objects and can easily fall into local minima solutions [10].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Point Cloud Registration Techniques on Scanned Physical Objects

Denayer,

De Winter,

Bernardes

et al. 2024

Sensors

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This paper presents a comparative analysis of six prominent registration techniques for solving CAD model alignment problems. Unlike the typical approach of assessing registration algorithms with synthetic datasets, our study utilizes point clouds generated from the Cranfield benchmark. Point clouds are sampled from existing CAD models and 3D scans of physical objects, introducing real-world complexities such as noise and outliers. The acquired point cloud scans, including ground-truth transformations, are made publicly available. This dataset includes several cleaned-up scans of nine 3D-printed objects. Our main contribution lies in assessing the performance of three classical (GO-ICP, RANSAC, FGR) and three learning-based (PointNetLK, RPMNet, ROPNet) methods on real-world scans, using a wide range of metrics. These include recall, accuracy and computation time. Our comparison shows a high accuracy for GO-ICP, as well as PointNetLK, RANSAC and RPMNet combined with ICP refinement. However, apart from GO-ICP, all methods show a significant number of failure cases when applied to scans containing more noise or requiring larger transformations. FGR and RANSAC are among the quickest methods, while GO-ICP takes several seconds to solve. Finally, while learning-based methods demonstrate good performance and low computation times, they have difficulties in training and generalizing. Our results can aid novice researchers in the field in selecting a suitable registration method for their application, based on quantitative metrics. Furthermore, our code can be used by others to evaluate novel methods.

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Alignment of 3D point cloud, CAD model, real-time camera view and partial point cloud for pipeline retrofitting application

Cited by 6 publications

References 9 publications

Fusion of Airborne LiDAR Point Clouds and Aerial Images for Heterogeneous Land-Use Urban Mapping

Fusion of Airborne LiDAR Point Clouds and Aerial Images for Heterogeneous Land-Use Urban Mapping

Machining tool identification utilizing temporal 3D point clouds

Comparison of Point Cloud Registration Techniques on Scanned Physical Objects

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