Laser Scanning Point Cloud Improvement by Implementation of RANSAC for Pipeline Inspection Application

Sepulveda-Valdez, Cesar; Sergiyenko, Oleg; Alaniz-Plata, Ruben; Núñez-López, José A.; Tyrsa, Vera; Flores-Fuentes, Wendy; Rodriguez-Quiñonez, Julio C.; Mercorelli, Paolo; Kolendovska, Marina; Kartashov, Vladimir; Miranda-Vega, Jesús Elías; Murrieta-Rico, Fabian N.

doi:10.1109/iecon51785.2023.10312684

Cited by 2 publications

(5 citation statements)

References 9 publications

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“…On the other hand, the authors of the present work have dedicated many years to 3D optical laser rotational scanning systems [3][4][5][6][7][8][9][10][11], and we have confirmed that our laser scanners, by default, naturally decrease their uncertainty, noise, and bias influence, and that their energetic losses and battery lifetime increase when operating in complete darkness. At the same time, in complete darkness, when traditional optical devices based on cameras of any type (APS, CMOS, CCD, omnidirectional fisheye, etc.)…”

Section: Introductionsupporting

confidence: 74%

“…According to the previous results presented in [11], the RANSAC algorithm helps in identifying a pipe's profile using accelerated outlier recognition. Furthermore, as new experiments show, outlier identification becomes a logical preliminary stage for defect detection and classification according to the algorithm presented in Section 3.…”

Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

“…RANSAC's application has two advantages: that already mentioned in [11] of a higher measured point's utilization (point cloud's efficiency), and the possibility of defect classification by analyzing the outliers' relative position concerning the circle profile's center. Experimental results in [11] demonstrate that using a threshold level of 0.5 mm increases the useful point quantity to 80.87% from a point cloud sample of 800 measurements. Nevertheless, in the same previous research, the percentage of potential defects in the total point cloud was 17.82%; by applying the detection algorithm developed in Section 3, the outlier percentage of potential defects results in 16.79%.…”

Section: Discussionmentioning

confidence: 99%

“…In our case, the model to be fit is a circle in (19), in order to find the diameter and center of the inspected pipeline. In addition, the objective of using the algorithm is the augmentation of the useful data in the point cloud to present the final result [11].…”

Section: Segmentation Using the Ransac-modified Algorithmmentioning

confidence: 99%

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Mathematical Modeling for Robot 3D Laser Scanning in Complete Darkness Environments to Advance Pipeline Inspection

Sepulveda-Valdez,

Sergiyenko,

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et al. 2024

Mathematics

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This paper introduces an autonomous robot designed for in-pipe structural health monitoring of oil/gas pipelines. This system employs a 3D Optical Laser Scanning Technical Vision System (TVS) to continuously scan the internal surface of the pipeline. This paper elaborates on the mathematical methodology of 3D laser surface scanning based on dynamic triangulation. This paper presents the mathematical framework governing the combined kinematics of the Mobile Robot (MR) and TVS. It discusses the custom design of the MR, adjusting it to use of robustized mathematics, and incorporating a laser scanner produced using a 3D printer. Both experimental and theoretical approaches are utilized to illustrate the formation of point clouds during surface scanning. This paper details the application of the simple and robust mathematical algorithm RANSAC for the preliminary processing of the measured point clouds. Furthermore, it contributes two distinct and simplified criteria for detecting defects in pipelines, specifically tailored for computer processing. In conclusion, this paper assesses the effectiveness of the proposed mathematical and physical method through experimental tests conducted under varying light conditions.

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Section: Introductionsupporting

confidence: 74%

Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Segmentation Using the Ransac-modified Algorithmmentioning

confidence: 99%

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Mathematical Modeling for Robot 3D Laser Scanning in Complete Darkness Environments to Advance Pipeline Inspection

Sepulveda-Valdez,

Sergiyenko,

Tyrsa

et al. 2024

Mathematics

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3D Data Processing and Entropy Reduction for Reconstruction from Low-Resolution Spatial Coordinate Clouds in a Technical Vision System

Alba Corpus,

Flores-Fuentes,

Sergiyenko

et al. 2024

Entropy

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This paper proposes an advancement in the application of a Technical Vision System (TVS), which integrates a laser scanning mechanism with a single light sensor to measure 3D spatial coordinates. In this application, the system is used to scan and digitalize objects using a rotating table to explore the potential of the system for 3D scanning at reduced resolutions. The experiments undertaken searched for optimal scanning windows and used statistical data filtering techniques and regression models to find a method to generate a 3D scan that was still recognizable with the least amount of 3D points, balancing the number of points scanned and time, while at the same time reducing effects caused by the particularities of the TVS, such as noise and entropy in the form of natural distortion in the resulting scans. The evaluation of the experimentation results uses 3D point registration methods, joining multiple faces from the original volume scanned by the TVS and aligning it to the ground truth model point clouds, which are based on a commercial 3D camera to verify that the reconstructed 3D model retains substantial detail from the original object. This research finds it is possible to reconstruct sufficiently detailed 3D models obtained from the TVS, which contain coarsely scanned data or scans that initially lack high definition or are too noisy.

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Laser Scanning Point Cloud Improvement by Implementation of RANSAC for Pipeline Inspection Application

Cited by 2 publications

References 9 publications

Mathematical Modeling for Robot 3D Laser Scanning in Complete Darkness Environments to Advance Pipeline Inspection

Mathematical Modeling for Robot 3D Laser Scanning in Complete Darkness Environments to Advance Pipeline Inspection

3D Data Processing and Entropy Reduction for Reconstruction from Low-Resolution Spatial Coordinate Clouds in a Technical Vision System

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