Development of a laser-based vision system for an underwater vehicle

Czajewski, Witold; Sluzek, Andrzej

doi:10.1109/isie.1999.801779

Cited by 16 publications

(5 citation statements)

References 2 publications

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“…The main difficulties in underwater vision are commonly attenuated by expensive equipment and complicated setups that help on providing cleaner 3D data. Examples of such sensors are sonars and lasers, which are commonly used for underwater imaging [1]- [4]. However, sonar input can be quite noisy and laser scanners suffer from distortion on the reconstructed cloud proportional to the motion of the sensor during the scan gathering.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Underwater 6D Pose Estimation from RGB

Sapienza¹,

Govi²,

Aldhaheri³

et al. 2023

Preprint

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Object pose estimation underwater allows an autonomous system to perform tracking and intervention tasks. Nonetheless, underwater target pose estimation is remarkably challenging due to, among many factors, limited visibility, light scattering, cluttered environments, and constantly varying water conditions. An approach is to employ sonar or laser sensing to acquire 3D data, but besides being costly, the resulting data is normally noisy. For this reason, the community has focused on extracting pose estimates from RGB input. However, the literature is scarce and exhibits low detection accuracy. In this work, we propose an approach consisting of a 2D object detection and a 6D pose estimation that reliably obtains object poses in different underwater scenarios. To test our pipeline, we collect and make available a dataset of 4 objects in 10 different real scenes with annotations for object detection and pose estimation. We test our proposal in real and synthetic settings and compare its performance with similar end-to-end methodologies for 6D object pose estimation. Our dataset contains some challenging objects with symmetrical shapes and poor texture. Regardless of such object characteristics, our proposed method outperforms stateof-the-art pose accuracy by ∼ 8%. We finally demonstrate the reliability of our pose estimation pipeline by doing experiments with an underwater manipulation in a reaching task.

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Section: Introductionmentioning

confidence: 99%

Model-Based Underwater 6D Pose Estimation from RGB

Sapienza¹,

Govi²,

Aldhaheri³

et al. 2023

Preprint

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“…Structured light systems are widely used in vision-based systems to perform a wide range of applications, such as 3D reconstruction, scanning, and range measurements [11][12][13]. Underwater laser vision systems have been studied in several previous works, using dot or line lasers [14][15][16]. In [15], a methodology for defining the position vector of an ROV is proposed using the ROV camera signal and the information provided by two laser pointers.…”

Section: Introductionmentioning

confidence: 99%

“…In [15], a methodology for defining the position vector of an ROV is proposed using the ROV camera signal and the information provided by two laser pointers. In [14] a methodology of orientation estimation is also introduced, projecting a laser stripe on the image plane. A low-cost underwater laser range-finder based upon a simple camera and parallel laser line setup is proposed by [17], where the distance calculation is based on the pinhole camera model.…”

Section: Introductionmentioning

confidence: 99%

A Laser Vision System for Relative 3-D Posture Estimation of an Underwater Vehicle with Hemispherical Optics

2021

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This paper describes the development and experimental validation of algorithms for a novel laser vision system (LVS), suitable for measuring the relative posture from both solid and mesh-like targets in underwater environments. The system was developed in the framework of the AQUABOT project, a research project dedicated to the development of an underwater robotic system for inspection of offshore aquaculture installations. In particular, an analytical model for three-medium refraction that takes into account the nonlinear hemispherical optics for image rectification has been developed. The analytical nature of the model allows the online estimation of the refractive index of the external medium. The proposed LVS consists of three line-lasers within the field of view of the underwater robot camera. The algorithms that have been developed in this work provide appropriately filtered point-cloud datasets from each laser, as well as high-level information such as distance and relative orientation of the target with respect to the ROV. In addition, an automatic calibration procedure, along with the accompanying hardware for the underwater laser vision system has been developed to reduce the calibration overhead required by regular maintenance operations for underwater robots operating in seawater. Furthermore, a spatial image filter was developed for discriminating between mesh and non-mesh-like targets in the LVS measurements. Finally, a set of experiments was carried out in a controlled laboratory environment, as well as in real conditions at offshore aquaculture installations demonstrating the performance of the system.

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“…Welding automation technology mainly includes weld seam-tracking technology and weld beadforming control technology; these two technologies were thoroughly studied in the onshore welding. However, the automatic control of underwater welding mainly focuses on the weld seam-tracking technology (Rong-Hua et al, 2010;Zhou et al, 2010;Machida, 1994, 1996;Czajewski and Sluzek, 1999;Xiao et al, 2009), and the weld bead-forming control is rarely seen. However, the underwater welding, especially the underwater wet welding, has very poor working conditions: the work piece is directly placed in the water; the weld arc is protected only by the air; and the steam produced by the burning of welding materials and the work piece, coupled with the impact of water cooling and water pressure, the short-circuit time and frequency, will increase, as will the spatter and arc extinguish rate.…”

Section: Introductionmentioning

confidence: 99%

Modeling of underwater wet welding process based on visual and arc sensor

Chen

Feng

2014

Industrial Robot: An International Journal

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Purpose – The purpose of this paper was to use visual and arc sensors to simultaneously obtain the underwater wet welding information, and a weld seam-forming model was made to predict the weld seam's geometric parameters. It is difficult to obtain a fine welding quality in underwater welding because of the intense disturbances of the water environment. To automatically control the welding quality, the weld seam-forming model should first be established. Thus, the foundation was laid for automatically controlling the underwater welding seam-forming quality. Design/methodology/approach – Visual and arc sensors were used simultaneously to obtain the weld seam image, current and voltage information; then signal algorithms were used to process the information, and the back propagation (BP) neural network was used to model the process. Findings – Experiment results showed that the BP neural network model could precisely predict the weld seam-forming parameters of underwater wet welding. Originality/value – A weld seam-forming model of underwater wet welding process was made; this laid the foundation for establishing a controller for controlling the underwater wet welding process automatically.

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Development of a laser-based vision system for an underwater vehicle

Cited by 16 publications

References 2 publications

Model-Based Underwater 6D Pose Estimation from RGB

Model-Based Underwater 6D Pose Estimation from RGB

A Laser Vision System for Relative 3-D Posture Estimation of an Underwater Vehicle with Hemispherical Optics

Modeling of underwater wet welding process based on visual and arc sensor

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