Recognition of pipeline geometry by using monocular camera and PSD sensors

Choi, Yun Seok; Kim, Ho Moon; Mun, Hyeong Min; Lee, Yoon Geon; Choi, Hyouk Ryeol

doi:10.1007/s11370-017-0221-1

Cited by 13 publications

(11 citation statements)

References 19 publications

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“…The most advanced in-pipe autonomous robots for an unknown pipe are the MRINSPECT series [8], they utilise 'Position Sensitve Devices' (PSDs), lasers and a CCD camera to provide corner directional information. Early versions used a CCD camera to detect the patterns of shadows for identifying landmarks within the pipe [30].…”

Section: In-pipe Sensingmentioning

confidence: 99%

“…Both the PSDs and camera work in fixed radius pipe networks, although a method determining radius has been proposed [8]. This system has a mean error of 2.84 • for predicting the angle of the corner direction.…”

Section: In-pipe Sensingmentioning

confidence: 99%

“…Despite MRINSPECT's [8] method being computationally light, the requirement of a camera and imaging processing would add an additional unwanted load to the system. They also utilised 6 -8 'Infrared' (IR) sensors for the PSD system this would adding further load in sampling and powering the sensors themselves.…”

Section: In-pipe Sensingmentioning

confidence: 99%

“…This metric is used to allow it to be compared with MRINSPECT VI [8] which is being used as the benchmark for this system. To see the impact the error has on the output of the system, the DU velocities will be calculated.…”

Section: Single Passmentioning

confidence: 99%

“…The MRINSPECT VI robot [8] and Kim et al's system [26] are being used as the benchmark for this prediction, they have a mean error (¯ θ ) of 2.84 • and 0.64 • respectively in their prediction. Comparing this to the results presented in Section 5, the mean error of the real system is¯ θ = 4.69 • which shows that both the sensor suits have a more accurate corner detection.…”

Section: Comparison Other Detection Systemsmentioning

confidence: 99%

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Elbow Detection in Pipes for Autonomous Navigation of Inspection Robots

Brown

Carrasco

Watson

et al. 2018

J Intell Robot Syst

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Nuclear decommissioning is a global challenge with high costs associated with it due to the hazardous environments created by radioactive materials. Most nuclear decommissioning sites contain significant amounts of pipework, the majority of which is uncharacterised with regards radioactive contamination. If there is any uncertainty as to the contamination status of a pipe, it must be treated as contaminated waste, which can lead to very high disposal costs. To overcome this challenge, an in-pipe autonomous robot for characterisation is being developed. One of the most significant mechatronic challenges with the development of such a robot is the detection of elbows in the unknown pipe networks to allow the robotic system to autonomously navigate around them. This paper presents a novel method of predicting the direction and radius of the corner using whisker-like sensors. Experiments have shown that the proposed system has a mean error of 4.69 • in the direction estimation.

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Section: In-pipe Sensingmentioning

confidence: 99%

Section: In-pipe Sensingmentioning

confidence: 99%

Section: In-pipe Sensingmentioning

confidence: 99%

Section: Single Passmentioning

confidence: 99%

Section: Comparison Other Detection Systemsmentioning

confidence: 99%

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