• The Journal of Korea Robotics Society
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• v.16 no.4
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• pp.306-312
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• 2021

This paper describes localization of autonomous underwater vehicles(AUV), which can be used when some navigation sensor data are an outlier. In that situation, localization through existing navigation algorithms causes problems in long-range localization. Even if an outlier sensor data occurs once, problems of localization will continue. Also, if outlier sensor data is related to azimuth (direction of AUV), it causes bigger problems. Therefore, a parallel localization module, in which different algorithms are performed in a normal and abnormal situation should be designed. Before designing a parallel localization module, it is necessary to study an effective method in the abnormal situation. So, we propose a localization method through machine learning. For this method, a learning model consists of only Fully-Connected and trains through randomly contaminated real sea data. The ground truth of training is displacement between subsequent GPS data. As a result, average error in localization through the learning model is 0.4 times smaller than the average error in localization through the existing navigation algorithm. Through this result, we conclude that it is suitable for a component of the parallel localization module.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.70-77
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• 2015

This paper proposes an application of unscented Kalman filter(UKF) for localization of an underwater robot. The method compares the bathymetric measurement from the robot with the seabed terrain information. For the measurement of bathymetric range to seabed, it uses a DVL which typically yields four range data together with velocity of the robot. Usual extended Kalman filter is not appropriated for application in case of terrain navigation, since it is not feasible to derive Jacobian for the bathymetric range measurement. Though particle filter(PF) is a nice solution which doesn't require Jacobian and can deal with non-linear and non-Gaussian system and measurement, it suffers from heavy computational burden. The paper compares the localization performance and the computation time of the UKF approach and PF approach. Though there have been some UKF methods which are used for underwater navigation, application of the UKF for bathymetric localization is rare. Especially, the proposed method uses only four range data whereas many of the bathymetric navigation methods have used multibeam sonar which yields hundreds of scanned range data. The result shows feasibility of the UKF approach for terrain-based navigation using small numbers of range data.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.188-195
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• 2024

This paper proposes a photorealistic real-time dense 3D mapping system that utilizes a neural network-based image enhancement method and mesh-based map representation. Due to the characteristics of the underwater environment, where problems such as hazing and low contrast occur, it is hard to apply conventional simultaneous localization and mapping (SLAM) methods. At the same time, the behavior of Autonomous Underwater Vehicle (AUV) is computationally constrained. In this paper, we utilize a neural network-based image enhancement method to improve pose estimation and mapping quality and apply a sliding window-based mesh expansion method to enable lightweight, fast, and photorealistic mapping. To validate our results, we utilize real-world and indoor synthetic datasets. We performed qualitative validation with the real-world dataset and quantitative validation by modeling images from the indoor synthetic dataset as underwater scenes.

• The Journal of the Acoustical Society of Korea
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• v.23 no.7
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• pp.514-520
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• 2004

There are many techniques for underwater source localization. These are the methods based on TDOA (Time Difference Of Arrival) estimation. beamforming techniques and high resolution techniques, etc. In this Paper we estimate the underwater source position using MCPSP (Modified Cross Power Spectrum Phase) function that is calculated on frequency domain using sensors of small number. However, the performances of the localizing method based on MCPSP function drops greatly in the case of CW (Continuous Wave) signal . In this Paper we proposed the TDOA estimation method for pulsed CW signal. In the Proposed method we composed of new segment including a edge of ping. This segment was computed by short-time energy detection. With theoretical representation the performances of the proposed method were analyzed under various environment.

• The Journal of the Acoustical Society of Korea
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• v.19 no.2
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• pp.68-72
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• 2000

Matched field processing technique is performed by considering complex underwater environments. Specially, the performance of minimum variance processor is greatly degraded by eigenvalue problem. In this paper, we propose the minimum variance matched field processor using shaping matrix. This shaping matrix makes that the input covariance matrix is invertible and enhances the desired acoustic source component. It was proved effectively range/depth localization of the proposed method with simulated data and vertical array data collected by NATO SACLANT Center north of the island of Elba off the Italian west coast.

• The Journal of Korea Robotics Society
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• v.9 no.4
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• pp.197-205
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• 2014

As computer vision algorithms are developed on a continuous basis, the visual information from vision sensors has been widely used in the context of simultaneous localization and mapping (SLAM), called visual SLAM, which utilizes relative motion information between images. This research addresses a visual SLAM framework for online localization and mapping in an unstructured seabed environment that can be applied to a low-cost unmanned underwater vehicle equipped with a single monocular camera as a major measurement sensor. Typically, an image motion model with a predefined dimensionality can be corrupted by errors due to the violation of the model assumptions, which may lead to performance degradation of the visual SLAM estimation. To deal with the erroneous image motion model, this study employs a local bundle optimization (LBO) scheme when a closed loop is detected. The results of comparison between visual SLAM estimation with LBO and the other case are presented to validate the effectiveness of the proposed methodology.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.169-172
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• 1999

Matched field processing technique is performed by considering complex underwater environments. Specially, tile performance of minimum variance processor is greatly degraded by eigenvalue problem. In this paper, we .propose the minimum valiance matched field processor using shaping matrix. This shaping matrix makes that the input covariance matrix is invertible and enhances the desired acoustic source component. It was proved effectively range/depth localization of the proposed method with vertical array data collected by NATO SACLANT Center north of the island of Elba off the Italian west coast.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.418-422
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• 2013

This paper has considered the location estimation problem in two dimension space by using a non-linear filter under non-Gaussian noise in underwater acoustic sensor networks(UASNs). Recently, the extended Kalman filter (EKF) is widely used in location estimation. However, the EKF has a lot of problems in the non-linear system under the non-gaussian noise environment like underwater environment. In this paper, we propose the improved Two-Dimension Particle Filter (TDPF) using the re-interpretation distribution techniques based on the maximum likelihood (ML). Through the simulation, we compared and analyzed the proposed TDPF with the EKF under the non-Gaussian underwater sensor networks. Finally, we determined that the TDPF's result shows more accurate localization than EKF's result.

• Ocean Systems Engineering
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• v.9 no.4
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• pp.391-407
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• 2019

Tracking the location (position) of a surface or underwater marine vehicle is important as part of guidance and navigation. While the Global Positioning System (GPS) works well in an open sea environment but its use is limited whenever testing scaled-down models of such vehicles in the laboratory environment. This paper presents the design, development and implementation of a low energy ultrasonic augmented single beacon-based localization technique suitable for such requirements. The strategy consists of applying Extended Kalman Filter (EKF) to achieve location tracking from basic dynamic distance measurements of the moving model from a fixed beacon, while on-board motion sensor measures heading angle and velocity. Iterative application of the Extended Kalman Filter yields x and y co-ordinate positions of the moving model. Tests performed on a free-running ship model in a wave basin facility of dimension 30 m by 30 m by 3 m water depth validate the proposed model. The test results show quick convergence with an error of few centimeters in the estimated position of the ship model. The proposed technique has application in the real field scenario by replacing the ultrasonic sensor with industrial grade long range acoustic modem. As compared with the existing systems such as LBL, SBL, USBL and others localization techniques, the proposed technique can save deployment cost and also cut the cost on number of acoustic modems involved.

• The Journal of Korea Robotics Society
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• v.12 no.2
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• pp.173-183
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• 2017

This paper proposes an integrated positioning system to localize a moving object in the shadow-area that exists in the water tank. The new water tank for underwater robots is constructed to evaluate the navigation performance of underwater vehicles. Several sensors are integrated in the water tank to provide the position information of the underwater vehicles. However there are some areas where the vehicle localization becomes very poor since the very limited sensors such as sonar and depth sensors are effective in underwater environment. Also there are many disturbances at sonar data. To reduce these disturbances, an extended Kalman filter has been adopted in this research. To localize the underwater vehicles under the hostile situations, a SVR (Support Vector Regression) has been systematically applied for estimating the position stochastically. To demonstrate the performance of the proposed algorithm (an extended Kalman filter + SVR analysis), a new UI (User Interface) has been developed.