• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.8
• /
• pp.667-675
• /
• 2013

This paper presents vision-based techniques for underwater landmark detection, map-based localization, and SLAM (Simultaneous Localization and Mapping) in structured underwater environments. A variety of underwater tasks require an underwater robot to be able to successfully perform autonomous navigation, but the available sensors for accurate localization are limited. A vision sensor among the available sensors is very useful for performing short range tasks, in spite of harsh underwater conditions including low visibility, noise, and large areas of featureless topography. To overcome these problems and to a utilize vision sensor for underwater localization, we propose a novel vision-based object detection technique to be applied to MCL (Monte Carlo Localization) and EKF (Extended Kalman Filter)-based SLAM algorithms. In the image processing step, a weighted correlation coefficient-based template matching and color-based image segmentation method are proposed to improve the conventional approach. In the localization step, in order to apply the landmark detection results to MCL and EKF-SLAM, dead-reckoning information and landmark detection results are used for prediction and update phases, respectively. The performance of the proposed technique is evaluated by experiments with an underwater robot platform in an indoor water tank and the results are discussed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.29 no.3
• /
• pp.275-281
• /
• 2011

To acquire an underwater topographic information is necessary for the design and construction of structures in marine and inland water. It consists of water depth information by bathymetric survey and underwater bottom topography image information can be obtained by side scan sonar in different ways. For the purpose of providing high quality data by means of engineering site survey, it is necessary to apply simultaneous acquisition of two information and carry out the integrated interpretation to each other. The present research aims to obtain information of the underwater topography and water depth at the same time using interferometer technique, and to validate interferometer technique with accuracy estimation.

• Journal of information and communication convergence engineering
• /
• v.14 no.3
• /
• pp.200-206
• /
• 2016

Images can be obtained by collecting rays from objects. The characteristics of electromagnetic wave propagation depend on the medium. In particular, in an underwater imaging system, the interface between air and water must be considered. Further, reflection and transmission coefficients can be found by using electromagnetic theory. Because of the fact that the values of these coefficients differ according to the media, the recorded light intensities will change. A color image sensor has three different color channels. Therefore, the reflection and transmission coefficients have to be calculated individually. Thereafter, by using these coefficients, we can compensate for the color information of underwater objects. In this paper, we present a method to compensate for the color information of underwater objects by using electromagnetic wave propagation theory. To prove our method, we conducted optical experiments and evaluated the quality of the compensated image by a metric known as mean square error.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.746-751
• /
• 2003

A method of measuring the length of defects on the wall of the spent nuclear fuel pool using the image processing and a laser slit beam is proposed. Since the defect monitoring camera is suspended by a crane and hinged to the crane hook, the camera viewing direction can not be adjusted to the orientation that is exactly perpendicular to the wall. Thus, the image taken by the camera, which is horizontally rotated along the axis of the camera supporting beam, is distorted and thus, the precise length can not be measured. In this paper, by using the LASER slit beam generator, the horizontally rotated angle of the camera is estimated. Once the angle is obtained, the distorted image can be easily reconstructed to the image normal to the wall. The estimation algorithm adopts a 3-dimensional coordinate transformation of the image plane where both the laser slit beam and the original image of the defects exist. The estimation equation is obtained by using the information of the beam projected on the wall and the parameters of this equation are experimentally obtained. With this algorithm, the original image of the defect taken at arbitrary rotated angle can be reconstructed to an image normal to the wall. From the result of a series of experiments, the accuracy of the defect is measured within 0.6 and 1.3 % error bound of real defect size in the air and underwater, respectively under 30 degree of the inclined angle of the laser slit beam generator. Also, the error increases as the inclined angle increases upto 60 degree. Over this angle, the defect length can not be measured since the defect image disappears. The proposed algorithm enables the accurate measurement of the defect length only by using a single camera and a laser slit beam.

• The Journal of the Acoustical Society of Korea
• /
• v.37 no.1
• /
• pp.12-20
• /
• 2018

Efforts have been made to reconstruct low-resolution underwater images to high-resolution ones by using the image SR (Super-Resolution) method, all to improve efficiency when acquiring side-scan sonar images. As side-scan sonar images are similar with the optical images with respect to exploiting 2-dimensional signals, conventional image restoration methods for optical images can be considered as a solution. One of the most typical super-resolution methods for optical image is a sparse coding and there are studies for verifying applicability of sparse coding method for underwater images by analyzing sparsity of underwater images. Sparse coding is a method that obtains recovered signal from input signal by linear combination of dictionary and sparse coefficients. However, it requires huge computational load to accurately estimate sparse coefficients. In this study, a sparse coding based underwater image super-resolution method is applied while a selective reconstruction method for object region is suggested to reduce the processing time. For this method, this paper proposes an edge detection and object and non object region classification method for underwater images and combine it with sparse coding based image super-resolution method. Effectiveness of the proposed method is verified by reducing the processing time for image reconstruction over 32 % while preserving same level of PSNR (Peak Signal-to-Noise Ratio) compared with conventional method.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.23 no.7
• /
• pp.926-932
• /
• 2017

Image distortion can occur when photographing deep sea targets with an optical camera. This problem arises because sunlight is not sufficiently transmitted due to seawater and various floating particles of dust. Particularly, color distortion takes place, causing green and blue color channels to be over emphasized due to water depth, while distortion of boundaries also occurs due to light refraction by seawater and floating particles of dust. These distortions degrade the overall quality of underwater images. In this paper, we analyze underwater images of the bottom of vessels. Based on the results, we propose a technique for color correction and edge enhancement. Experimental results show that the proposed method increases edge clarity by 3.39 % compared to the effective edges of the original underwater image. In addition, a quantitative evaluation and subjective image quality evaluation were concurrently performed. As a result, it was confirmed that object boundaries became clear with color correction. The color correction and contour enhancement method proposed in this paper can be applied in various fields requiring underwater imaging in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.475-476
• /
• 2018

In this paper, we propose a method to measure the number of biofouling attached to underwater structures. This method measures the number of biofouling based on digital images captured in underwater. The number of biofouling was measured after correcting the image quality of underwater images for accurate population counting. In order to measure the number of biofouling, Maxima value in the image was found.

• Journal of Ocean Engineering and Technology
• /
• v.17 no.1
• /
• pp.1-7
• /
• 2003

Autonomous underwater vehicles (AUVs) are unmanned, underwater vessels that are used to investigate sea environments in the study of oceanography. Docking systems are required to increase the capability of the AUVs, to recharge the batteries, and to transmit data in real time for specific underwater works, such as repented jobs at sea bed. This paper presents a visual :em control system used to dock an AUV into an underwater station. A camera mounted at the now center of the AUV is used to guide the AUV into dock. To create the visual servo control system, this paper derives an optical flow model of a camera, where the projected motions of the image plane are described with the rotational and translational velocities of the AUV. This paper combines the optical flow equation of the camera with the AUVs equation of motion, and deriver a state equation for the visual servo AUV. Further, this paper proposes a discrete-time MIMO controller, minimizing a cost function. The control inputs of the AUV are automatically generated with the projected target position on the CCD plane of the camera and with the AUVs motion. To demonstrate the effectiveness of the modeling and the control law of the visual servo AUV simulations on docking the AUV to a target station are performed with the 6-dof nonlinear equations of REMUS AUV and a CCD camera.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
• /
• 2000.10a
• /
• pp.90-95
• /
• 2000

This paper presents the vergence control of a parallel stereo camera and its application to underwater stereo camera to enhance the working efficiency of underwater vehicles that equips with manipulators in seabed operation. The stereo camera consists of two parallel lenses mounted on a lateral moving base and two CCD cameras mounted on a longitudinal moving base, which is embedded in a small pressure canister for underwater application. Because the lateral shift is related to the backward shift with a nonlinear relation, only one control input is needed to control the vergence and focus of the camera with a special driving device. We can get a clear stereo vision with the camera for all the range of objects in air and in water, especially in short range objects. The control system of the camera is so simple that we are able to realize a small stereo camera system and to apply it to a stereo vision system for underwater vehicles. This paper also shows how to acquire the distance information of an underwater object with this stereo camera. Whenever we focus on an underwater object with the camera, we can obtain the three-dimensional images and the distance information in real-time.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.6
• /
• pp.1379-1386
• /
• 2018

Conventional underwater cameras used in fish farms can only shoot limited areas and are vulnerable to underwater contamination. There is also a problem with contaminated farms as surplus residues are deposited as a result of feed supply to farms' nets. This paper proposes underwater drones for underwater monitoring of fish farms and cleaning nets. If underwater drones are used for management of fish farms, underwater imaging, monitoring and cleaning of fish farms' nets can be possible. By using this technology, data can be collected by detecting changes in the environment of a fish farm and responding to changes that occur within a fish farm based on the data. In addition, the establishment of an integrated control system will enable to build efficient and stable smart farms.