Stereo-vision-based AUV navigation system for resetting the inertial navigation system error

Hsu, Horng Yi; Toda, Yuichiro; Yamashita, Kohei; Watanabe, Keigo; Sasano, Masahiko; Okamoto, Akimitsu; Inaba, Shogo; Minami, Mamoru

doi:10.1007/s10015-021-00720-z

Cited by 12 publications

(6 citation statements)

References 17 publications

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“…This setup allowed for accurate three-dimensional positioning between the AUH and the landing platform using a binocular stereo vision algorithm, ensuring high-precision and reliable underwater docking for AUH. Okayama University [39] employed three spherical markers of different colors (red, green, and blue), each containing LEDs, to create a three-dimensional marker system. These spheres, with a diameter of 40 millimeters, were equipped with adjustable variable resistors to adapt to varying lighting conditions.…”

Section: Underwater Visual Navigation and Docking Based On Binocular ...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 Navigation and Docking Based On Binocular ...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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“…A stereo-vision-based AUV navigation system to reset the integrated INS error was proposed in the paper "Stereo-vision-based AUV navigation system for resetting the inertial navigation system error" by [10]. Using INS and the stereo-vision system, AUV navigation and docking station return tests were carried out in a test tank.…”

Section: Literature Reviewmentioning

confidence: 99%

Accurate Position Estimation of Autonomous Underwater Vehicles by a Kalman Filter Using an Inertial Navigation Sensor

Raza,

Arshad,

Ashraf

et al. 2023

Ieec 2023

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“…The use of artificial tags in underwater operations has been investigated by many researchers lately because they function in GNSS-denied environments. In this context, Myint et al [27] and Hsu et al [28] developed vision-based docking and recharging systems for autonomous underwater vehicles using stereo vision. Both used a 3-D model-based matching method combined with custom 3-D markers to estimate the pose of the vehicle.…”

Section: A Related Workmentioning

confidence: 99%

Development and Experimental Validation of Visual-Inertial Navigation for Auto-Docking of Unmanned Surface Vehicles

2023

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Docking is a safety-critical operation for autonomous surface vehicles and requires highly accurate navigation signals. Since Global Navigation Satellite Systems (GNSS) can be unreliable and inaccurate in urban environments, other sensors should be considered for increased redundancy and reliability. To this end, we present a low-cost visual-inertial navigation system that we can use for automatic docking of small vehicles. The proposed system produces state estimates of the vehicle, including position, velocity, and attitude, based on raw image and inertial data. To simplify the navigation task, we use easily identifiable tags as a reference on the dockside. When the vehicle approaches the dock, a visual fiducial system recognizes the tags and estimates the relative pose between the camera onboard the vehicle and the tags at the dockside. The camera-tag pose and inertial data are then fused using an error-state Kalman filter for robust state estimation of the vehicle. For benchmarking, we use an unmanned surface vehicle equipped with a dual-antenna realtime kinematic GNSS receiver for accurate positioning and heading. We show that the proposed method performs well on regular and adverse weather data. Finally, we demonstrate that the proposed method performs well in feedback control through field experiments and can supplement traditional navigation systems for docking operations.INDEX TERMS Autonomous docking, fiducial tags, unmanned surface vehicles, visual-inertial state estimation.

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Stereo-vision-based AUV navigation system for resetting the inertial navigation system error

Cited by 12 publications

References 17 publications

Review of underwater visual navigation and docking: advances and challenges

Review of underwater visual navigation and docking: advances and challenges

Accurate Position Estimation of Autonomous Underwater Vehicles by a Kalman Filter Using an Inertial Navigation Sensor

Development and Experimental Validation of Visual-Inertial Navigation for Auto-Docking of Unmanned Surface Vehicles

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