Development, Calibration, and Image Processing of Underwater Structured Light Vision System: A Survey

Fan, Junfeng; Wang, Xuan; Zhou, Chao; Ou, Yaming; Jing, Fengshui; Hou, Zeng-Guang

doi:10.1109/tim.2023.3235420

Cited by 19 publications

(6 citation statements)

References 129 publications

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“…Structured light visual (SLV) sensors, which consist of a structured light projector and a monocular camera or binocular camera, can calculate the depth of a light strip in an image by the laser triangulation algorithm. This is widely used in measurement areas such as automatic agriculture [13], industry inspection [14,15] and robot indoor navigation [16,17], due to the advantages of simple construction, high accuracy and low cost. An SLV sensor mounted on an MAV can scan the environment agilely with rotation of the MAV.…”

Section: Introductionmentioning

confidence: 99%

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Wang,

Ding,

Wang

et al. 2024

Remote Sensing

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Considering that laser scan stations are expensive and heavy for the indoor environment reconstruction of unmanned systems, a low-cost and light reconstruction system is proposed in this research. The system consists of a cross-structured light visual (SLV) sensor and an inertial navigation system (INS). The cross-SLV sensor is used to scan the surroundings and to estimate the angle change between two adjacent measurements. To improve the robustness and accuracy of the angle measurement, a Kalman Filter (KF) with simple construction is designed to fuse the inertial information from the INS. The factors which influence ranging accuracy are analyzed and ranging experiments show that the SLV sensor has an accuracy of higher than 2% when the distance is less than 4 m. Then the reconstruction results of a kitchen and corridor show that the error of most points is less than 50 mm for either kitchen (94%) or corridor (85%), and the mean errors and standard deviations of kitchen and corridor are less than 20 mm and 30 mm, respectively.

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Section: Introductionmentioning

confidence: 99%

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Wang,

Ding,

Wang

et al. 2024

Remote Sensing

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“…Underwater optical information, characterized by its rich colors, distinct boundaries, and diverse dimensions and types of information, significantly enhances the precision of resource exploration efforts. It facilitates the discovery of deep-water biological entities and the exploration of seabed minerals, positioning itself as a critical medium for underwater data collection and one of the essential methods for accessing marine resource information [3,4].…”

Section: Introductionmentioning

confidence: 99%

Breakthrough Underwater Physical Environment Limitations on Optical Information Representations: An Overview and Suggestions

Li,

Zhang,

Zhang

et al. 2024

JMSE

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Underwater optics have seen a notable surge of interest in recent years, emerging as a critical medium for conveying information crucial to underwater resource exploration, autonomous underwater vehicle navigation, etc. The intricate dynamics of underwater optical transmission, influenced by factors such as the absorption by the water and scattering by multiple particles, present considerable challenges. One of the most critical issues is that the optical information representation methods fail to take into account the impact of the underwater physical environment. We conducted a comprehensive review and analysis of recent advancements in underwater optical transmission laws and models. We summarized and analyzed relevant research on the effects of underwater particles and turbulence on light and analyzed the polarization effects in various environments. Then, the roles of various types of underwater optical propagation models were analyzed. Although optical models in complex environments are still mostly based on Monte Carlo methods, many underwater optical propagation mechanisms have been revealed and can promote the impacts of optical information expression. We delved into the cutting-edge research findings across three key domains: the enhancement of underwater optical image quality, the 3D reconstruction from monocular images, and the underwater wireless optical communication, examining the pivotal role played by light transmission laws and models in these areas. Drawing upon our extensive experience in underwater optics, including underwater optical sensor development and experiments, we identified and underscored future directions in this field. We advocate for the necessity of further advancements in the comprehension of underwater optical laws and physical models, emphasizing the importance of their expanded application in underwater optical information representations. Deeper exploration into these areas is not only warranted but essential for pushing the boundaries of current underwater optical technologies and unlocking new potential for their application in underwater optical sensor developments, underwater exploration, environmental monitoring, and beyond.

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“…There are many challenges in the design and usage of an underwater 3D imaging system [ 6 ]. The difficulty of achieving the desired accuracy [ 7 , 8 ] of the imaging system is caused by image distortions due to the physical properties of light propagation in an underwater environment.…”

Section: Introductionmentioning

confidence: 99%

“…In an SL imaging system, a projector projects a code onto a surface of an observed object, which is then observed by one or more cameras to reconstruct the 3D shape. Therefore, an underwater 3D imaging system comprising one or more cameras and one or more projectors is an active SL 3D imaging device for which there are many reported uses in underwater imaging [ 1 , 3 , 6 , 13 , 14 , 15 ]. Next, underwater SL systems may be classified depending on the shape of the air-to-water interface, usually a protective glass, which may be flat or curved [ 9 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Calibration of a Structured Light Imaging System in Two-Layer Flat Refractive Geometry for Underwater Imaging

Zoraja

Petković

Forest

et al. 2023

Sensors

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The development of a robust 3D imaging system for underwater applications is a crucial process in underwater imaging where the physical properties of the underwater environment make the implementation of such systems challenging. Calibration is an essential step in the application of such imaging systems and is performed to acquire the parameters of the image formation model and to enable 3D reconstruction. We present a novel calibration method for an underwater 3D imaging system comprising a pair of cameras, of a projector, and of a single glass interface that is shared between cameras and projector(s). The image formation model is based on the axial camera model. The proposed calibration uses a numerical optimization of a 3D cost function to determine all system parameters, thus avoiding the minimization of re-projection errors which require numerically solving a 12th order polynomial equation multiple times for each observed point. We also propose a novel stable approach to estimate the axis of the axial camera model. The proposed calibration was experimentally evaluated on four different glass interfaces, wherein several quantitative results were reported, including the re-projection error. The achieved mean angular error of the system’s axis was under 6∘, and the mean absolute errors for the reconstruction of a flat surface were 1.38 mm for normal glass interfaces and 2.82 mm for the laminated glass interface, which is more than sufficient for application.

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Development, Calibration, and Image Processing of Underwater Structured Light Vision System: A Survey

Cited by 19 publications

References 129 publications

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Breakthrough Underwater Physical Environment Limitations on Optical Information Representations: An Overview and Suggestions

Calibration of a Structured Light Imaging System in Two-Layer Flat Refractive Geometry for Underwater Imaging

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