3D pipe reconstruction employing video information from mobile robots

Reyes, Alfredo; López-Juárez, Ismael; Osorio-Comparán, Román; Lefranc, Gastón

doi:10.1016/j.asoc.2018.11.016

Cited by 38 publications

(9 citation statements)

References 36 publications

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“…This algorithm is a combination of pose estimation and image unwrapping. The pose estimation and image unwrapping are implemented through a framework of MATLAB functions developed by the authors, it is noted that the optimization function used is part of the feature set developed by MathWorks [16]. The pose estimation utilizes the laser-profiler to calculate the probe position and orientation relative to the pipe.…”

Section: Non-centralized Automated Rvi Image Stitchingmentioning

confidence: 99%

A Novel Centralization Method for Pipe Image Stitching

et al. 2021

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The creation of unwrapped stitched images of pipework internal surfaces is being increasingly used to augment routine visual inspection. A significant challenge to the creation of these stitched images is the need to estimate the pose and position of the camera for each frame, which is often alleviated through the use of a mechanical centralizer to ensure the camera is held in the center of the pipe. This article proposes a novel method for image centralization and pose estimation, which is particularly beneficial to circumstances where mechanical centralization is impractical. The approach involves post-inspection centralization of the captured video, by first estimating the probe's position relative to the pipe, using an integrated laser ring projector combined with the camera sensor, and then using this position to unwrap the image, so it produces an undistorted view of the pipe interior (equivalent to unwrapping a centralized view). These unwrapped images are then stacked to produce a stitched image of the pipe interior. In this paper pose estimation was successfully demonstrated to have a 90% confidence interval of ±0.5 mm and ±0.5° in translation and rotation changes. This pose estimation is then used to create stitched images for both a visual test card image mounted inside a pipe and an aluminum pipe sample with artificial defects, in both cases demonstrating near equivalent results to those obtained using traditional mechanical centralization.

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Section: Non-centralized Automated Rvi Image Stitchingmentioning

confidence: 99%

A Novel Centralization Method for Pipe Image Stitching

et al. 2021

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“…Type-1 FLC, triangular and trapezoidal membership functions, is used to avoid the robot from turning over [113]. Also, [114] used a charge-coupled device (CCD) camera and image processing for locating elbow or junction and navigation using FLC.…”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

Localization, Mapping, Navigation, and Inspection Methods in In-Pipe Robots: A Review

Kazeminasab¹,

Sadeghi

Janfaza

et al. 2021

IEEE Access

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“…The second challenge lies in obtaining images of the internal walls of pipes. Generally, the RVI (remote visual inspection) technique is employed, utilizing inspection robots or handheld videoscopes to explore the pipes and capture images of their internal walls [ 5 , 6 , 7 ]. To address the cost-saving and accuracy enhancement concerns associated with human-dependent visual inspections, various automated image-processing techniques, including artificial intelligence, are required [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

An Inverse Perspective Mapping-Based Approach for Generating Panoramic Images of Pipe Inner Surfaces

Yoo

Lee

2023

Sensors

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We propose an algorithm for generating a panoramic image of a pipe’s inner surface based on inverse perspective mapping (IPM). The objective of this study is to generate a panoramic image of the entire inner surface of a pipe for efficient crack detection, without relying on high-performance capturing equipment. Frontal images taken while passing through the pipe were converted to images of the inner surface of the pipe using IPM. We derived a generalized IPM formula that considers the slope of the image plane to correct the image distortion caused by the tilt of the plane; this IPM formula was derived based on the vanishing point of the perspective image, which was detected using optical flow techniques. Finally, the multiple transformed images with overlapping areas were combined via image stitching to create a panoramic image of the inner pipe surface. To validate our proposed algorithm, we restored images of pipe inner surfaces using a 3D pipe model and used these images for crack detection. The resulting panoramic image of the internal pipe surface accurately demonstrated the positions and shapes of cracks, highlighting its potential utility for crack detection using visual inspection or image-processing techniques.

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3D pipe reconstruction employing video information from mobile robots

Cited by 38 publications

References 36 publications

A Novel Centralization Method for Pipe Image Stitching

A Novel Centralization Method for Pipe Image Stitching

Localization, Mapping, Navigation, and Inspection Methods in In-Pipe Robots: A Review

An Inverse Perspective Mapping-Based Approach for Generating Panoramic Images of Pipe Inner Surfaces

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