An Underwater Visual Navigation Method Based on Multiple ArUco Markers

Xu, Zhizun; Haroutunian, Maryam; Murphy, Alan J.; Neasham, Jeffrey; Norman, Rosemary

doi:10.3390/jmse9121432

Cited by 23 publications

(17 citation statements)

References 29 publications

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“…Within the 10-meter to 0.5-meter range, the algorithm accurately identifies targets, achieving a 100% attitude estimation success rate. Zhejiang University [31], using multiple ArUco markers, employs a single-marker attitude estimation noise model and an optimal algorithm for multiple markers to enhance navigation accuracy. Universidad de Girona [32] achieved an 8-meter lake positioning using a combination of active and passive optical beacons, employing four white LEDs in conjunction with a color camera.…”

Section: Underwater Visual Docking and Guidance Systems Based On Pass...mentioning

confidence: 99%

Review of underwater visual navigation and docking: advances and challenges

Li,

Sun

2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

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The widespread utilization of autonomous underwater vehicles in marine science and engineering has underscored the paramount importance of precise underwater navigation and docking capabilities for underwater robots. Underwater vision navigation systems serve as a pivotal technology, encompassing various domains such as computer vision, image processing, and machine learning. Nevertheless, they confront a series of challenges. This review aims to consolidate the most recent research advancements in underwater vision navigation systems, with a particular focus on critical aspects such as beacon design, monocular and binocular vision, and detection algorithms. At present, underwater beacons can be categorized into active and passive types, with the flexibility to choose between them contingent upon the specific task and environment. On the other hand, relative to the prevalent use of monocular vision, binocular vision systems offer the potential to furnish more accurate depth information, and the seamless integration of monocular and binocular vision systems enhances the precision and stability of navigation tasks. Concerning detection algorithms, traditional feature extraction methods and deep learning approaches each exhibit their merits and drawbacks, but deep learning methods demonstrate immense potential in complex underwater environments. By summarizing and analyzing the above research results, the application of each key technology of underwater vision navigation and docking is sorted out, and its further development direction is foreseen.

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Section: Underwater Visual Docking and Guidance Systems Based On Pass...mentioning

confidence: 99%

Review of underwater visual navigation and docking: advances and challenges

Li,

Sun

2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

View full text Add to dashboard Cite

show abstract

“…They served a double purpose: identify the kinematic parameters of the robot and test its trajectory tracking. Xu et al (2021) propose a strategy for underwater visual navigation based on multiple ArUco markers. Transformation matrices are used to derive the camera's position attached to the robot concerning different markers, whose positions are fed to a noise model due to the noise associated with underwater imaging.…”

Section: Fiducial Markers For Localizationmentioning

confidence: 99%

A robot localization proposal for the RobotAtFactory 4.0: A novel robotics competition within the Industry 4.0 concept

et al. 2022

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Robotic competitions are an excellent way to promote innovative solutions for the current industries’ challenges and entrepreneurial spirit, acquire technical and transversal skills through active teaching, and promote this area to the public. In other words, since robotics is a multidisciplinary field, its competitions address several knowledge topics, especially in the STEM (Science, Technology, Engineering, and Mathematics) category, that are shared among the students and researchers, driving further technology and science. A new competition encompassed in the Portuguese Robotics Open was created according to the Industry 4.0 concept in the production chain. In this competition, RobotAtFactory 4.0, a shop floor, is used to mimic a fully automated industrial logistics warehouse and the challenges it brings. Autonomous Mobile Robots (AMRs) must be used to operate without supervision and perform the tasks that the warehouse requests. There are different types of boxes which dictate their partial and definitive destinations. In this reasoning, AMRs should identify each and transport them to their destinations. This paper describes an approach to the indoor localization system for the competition based on the Extended Kalman Filter (EKF) and ArUco markers. Different innovation methods for the obtained observations were tested and compared in the EKF. A real robot was designed and assembled to act as a test bed for the localization system’s validation. Thus, the approach was validated in the real scenario using a factory floor with the official specifications provided by the competition organization.

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“…Although there are some visual positioning systems using the artificial makers [ 22 , 23 ], the markers are required to be set out beforehand.…”

Section: Related Work In Underwater Visual Positioningmentioning

confidence: 99%

An Underwater Positioning System for UUVs Based on LiDAR Camera and Inertial Measurement Unit

Yang

Jia

2022

Sensors

Self Cite

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Underwater positioning presents a challenging issue, because of the rapid attenuation of electronic magnetic waves, the disturbances and uncertainties in the environment. Conventional methods usually employed acoustic devices to localize Unmanned Underwater Vehicles (UUVs), which suffer from a slow refresh rate, low resolution, and are susceptible to the environmental noise. In addition, the complex terrain can also degrade the accuracy of the acoustic navigation systems. The applications of underwater positioning methods based on visual sensors are prevented by difficulties of acquiring the depth maps due to the sparse features, the changing illumination condition, and the scattering phenomenon. In the paper, a novel visual-based underwater positioning system is proposed based on a Light Detection and Ranging (LiDAR) camera and an inertial measurement unit. The LiDAR camera, benefiting from the laser scanning techniques, could simultaneously generate the associated depth maps. The inertial sensor would offer information about its altitudes. Through the fusion of the data from multiple sensors, the positions of the UUVs can be predicted. After that, the Bundle Adjustment (BA) method is used to recalculate the rotation matrix and the translation vector to improve the accuracy. The experiments are carried out in a tank to illustrate the effects and accuracy of the investigated method, in which the ultra-wideband (UWB) positioning system is used to provide reference trajectories. It is concluded that the developed positioning system is able to estimate the trajectory of UUVs accurately, whilst being stable and robust.

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An Underwater Visual Navigation Method Based on Multiple ArUco Markers

Cited by 23 publications

References 29 publications

Review of underwater visual navigation and docking: advances and challenges

Review of underwater visual navigation and docking: advances and challenges

A robot localization proposal for the RobotAtFactory 4.0: A novel robotics competition within the Industry 4.0 concept

An Underwater Positioning System for UUVs Based on LiDAR Camera and Inertial Measurement Unit

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