4-DOF pose estimation of a pipe crawling robot using a Collimated Laser, a conic mirror, and a fish-eye camera

Tezerjani, Abbasali Dehghan; Mehrandezh, Mehran; Paranjape, Raman

doi:10.1109/ssiai.2014.6806025

Cited by 6 publications

(11 citation statements)

References 6 publications

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“…Unique lookup tables were required for each pipe radii and specific camera calibrations. Dehghan et al [12] created a vision-based pose estimation technique for a pipe crawling robot. The system comprised of a collimated laser, a 45° conic mirror, and a camera with a fisheye lens mounted on a robot.…”

Section: A Robotically Deployed Rvimentioning

confidence: 99%

“…The system comprised of a collimated laser, a 45° conic mirror, and a camera with a fisheye lens mounted on a robot. Instead of using a lookup table as in [11], authors in [12] used Nelder-Mead optimization [13] to estimate pose from the laser-profiler. However, due to the unknown nature of the objective function derivatives, the pose estimation success rate depended on the optimization's initial seed and may reduce to as low as 30%.…”

Section: A Robotically Deployed Rvimentioning

confidence: 99%

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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: A Robotically Deployed Rvimentioning

confidence: 99%

Section: A Robotically Deployed Rvimentioning

confidence: 99%

A Novel Centralization Method for Pipe Image Stitching

et al. 2021

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“…is widely used in industry to widen the field of view, (e.g., [21,28] ). Fish-eye lenses are the most popular ones.…”

Section: Spatial Resolution Of Omnidirectional Imaging Systemsmentioning

confidence: 99%

Optimal Spatial Resolution of Omnidirectional Imaging Systems for Pipe Inspection Applications

Tezerjani¹,

Mehrandezh

Paranjape

2015

International Journal of Optomechatronics

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Achieving optimal spatial resolution in imaging systems plays a major role in the design of vision-based industrial inspection tools. Single-view omnidirectional imaging systems provide a cost-effective and computationally-traceable solution for real-time inspection of infrastructure with a favorable size factor. We formulate, for the first time, the spatial cylindrical resolution of omnidirectional Catadioptric and Dioptric imaging systems with the focus on pipe inspection applications. We also provide a design guideline to achieve the highest resolution in these systems. First, we deliver a comprehensive study on optimal resolution in Catadioptric imaging systems which consist of a perspective pinhole camera, a collimated laser as the light source, and a reflective surface (i.e., hyperbolic mirror). Variation of the spatial resolution in terms of the camera's focal length, the mirror curvature, and the relative position between the laser projector and the camera is fully investigated via simulation and experiments. Also, the optimal resolution in Dioptric systems, which consist of a camera with compound refractive lenses (i.e., fish-eye lens) is studied and compared with that in Catadioptric systems. Tests were conducted on a 40-cm-diameter PVC pipe in a controlled laboratory environment.

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“…Two major approaches to exteroceptive sensing have received significant attention from academia and industry: laser image processing and visual odometry . The laser image processing approach irradiates a laser beam pattern on the inner wall of the pipeline and estimates the pipe attributes or robot pose through the mathematical modeling of the reflected light captured at a camera image [ 13 , 14 , 15 , 16 , 17 , 18 ]. On the other hand, the visual odometry approach extracts visual features from a camera image, and estimates the relative robot pose by associating them with the corresponding features in the subsequent camera images [ 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

A Simultaneous Pipe-Attribute and PIG-Pose Estimation (SPPE) Using 3-D Point Cloud in Compressible Gas Pipelines

Nguyen

Park

Jeong

2023

Sensors

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An accurate estimation of pipe attributes, pose of pipeline inspection gauge (PIG), and downstream pipeline topology is essential for successful in-line inspection (ILI) of underground compressible gas pipelines. Taking a 3D point cloud of light detection and ranging (LiDAR) or time-of-flight (ToF) camera as the input, in this paper, we present the simultaneous pipe-attribute and PIG-pose estimation (SPPE) approach that estimates the optimal pipe-attribute and PIG-pose parameters to transform a 3D point cloud onto the inner pipe wall surface: major- and minor-axis lengths, roll, pitch, and yaw angles, and 2D deviation from the center of the pipe. Since the 3D point cloud has all spatial information of the inner pipe wall measurements, this estimation problem can be modeled by an optimal transformation matrix estimation problem from a PIG sensor frame to the global pipe frame. The basic idea of our SPPE approach is to decompose this transformation into two sub-transformations: The first transformation is formulated as a non-linear optimization problem whose solution is iteratively updated by the Levenberg–Marquardt algorithm (LMA). The second transformation utilizes the gravity vector to calculate the ovality angle between the geometric and navigation pipe frames. The extensive simulation results from our PIG simulator based on the robot operating system (ROS) platform demonstrate that the proposed SPPE can estimate the pipe attributes and PIG pose with excellent accuracy and is also applicable to real-time and post-processing non-destructive testing (NDT) applications thanks to its high computational efficiency.

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4-DOF pose estimation of a pipe crawling robot using a Collimated Laser, a conic mirror, and a fish-eye camera

Cited by 6 publications

References 6 publications

A Novel Centralization Method for Pipe Image Stitching

A Novel Centralization Method for Pipe Image Stitching

Optimal Spatial Resolution of Omnidirectional Imaging Systems for Pipe Inspection Applications

A Simultaneous Pipe-Attribute and PIG-Pose Estimation (SPPE) Using 3-D Point Cloud in Compressible Gas Pipelines

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