대형정치망에 있어서 등망을 중심으로하여 헛통으로부터 제1원통에 이르는 방어어군의 이동행동은 1992년 11월 9일부터 11월 13일까지 일본국 석천현 칠미시 연안에 있는 안단 정치망어장 3호망에서 스케닝 소나를 이용하여 조사하였고, 등망의 어획기능에 관해서 분석, 검토하였다. 이상에 의해 얻어진 결과를 요약하면 다음과 같다. 1. 방어어군의 출현은 헛통에서는 오전중, 제1원통에서는 오후에 가장 많이 볼 수 있었고, 망내에서의 체류시간은 길었다. 2. 등망을 통과하고 있는 어군의 규모는 헛통 및 제 1원통에 체류하는 어군보다 작았고, 이는 어군의 형상이 변하면서 등망을 통과하였기 때문이라고 생각된다. 3. 등망으로 향한 어군중에서 제 1원통에 입망하는 어군의 비율은 24%이고, 제 1원통에 내등망으로 향한 어군중 헛통으로 도피하는 어군의 비율은 27%였다. 이는 외등망의 구조가 원통으로 유도하는 기능이 충분하지 않다는 것을 의미하고 내등망의 구조는 출망을 저지하는 효과가 충분한 것으로 판단된다. 4. 정치망의 어획효율을 높이기 위해서는 어군의 입출망시각에 맞춘 양망작업이 필요하다. 조사기간중의 원통에 축적된 어군수로부터 그 다음날 아침의 양망은 시장유통과 노동력부족의 문제를 고려하여 적당하다고 생각할 수 있다.

The moving behaviour of Yellowtail Seriola quinqueradiata schools in the main-net of a large scale set-net was investigated in relation to the catching function of the funnel-net by a scanning sonar. The investigation was took place in the Kishihata set-net fishing ground located in Nanao city Ishigawa prefecture, Japan from Nov. 9 to Nov. 13, 1992. The obtained results are summarized as follows; 1. Fish schools showed the greatest number at the playground in the morning and at the bag-net in the afternoon. The fish schools remained long time in the main-net. 2. The rate of fish school through the funnel-net was smaller than that of fish school which is though the playground and bag-net. Because the Yellowtail school changed the shape of school in passing the funnel-net. 3. The rate of entering the bag-net was 24%, among the fish school heading to the outer funnel-net. But, the rate of escaping to the playground was 27%, among the fish school heading to the inner funnel-net. It seems that the structure of the outer funnel-net was not enough to lead the fish to the bag-net. However, the structure of the inner funnel-net was very effective at preventing escape. 4. It is appropriate to haul the net in the morning in considering the number of accumulated fish in the bag-net during the survey.

대형정치망의 원통내에 있어서 멸치어군의 이동행동은 1992년 12월 1일부터 12월 4일까지 일본국 석천현 칠미시 연안에 있는 안단 정치망 어장 3호망에서 스케닝 소나를 이용하여 실시하였는데, 그 결과를 요약하면 다음과 같다. 1. 2개의 원통시아에 어군의 이동행동은 주야간에 차이가 있었고, 밤보다는 낮에 더 많이 이동하였다. 2. 제2 원통에 멸치어군이 존재하였을 때, 제1원통에 있는 멸치어군이 제2 원통으로의 입망 비율은 78%였고, 제2 원통에 멸치어군의 존재는 제2원통에의 입망 비율에 영향을 미쳤다는 것을 시사하고 있다. 그러나, 제2원통에 조밀하게 멸치어군이 존재하였을 때, 제 2원통에 입망한 어군의 체류시간은 매우 짧았고, 제2 원통에서 어군의 밀도 상승은 어군의 도피 비율을 증가시킨다는 것을 시사하고 있다. 3. 멸치어군은 원통에 입망할 때, 대부분이 망의 중앙에서 먼바다 쪽으로 방향을 바꾸어 이동하였다.

정치망 내외에 있어서 정어리 대형군의 행동조사는 1992년 1월 29일부터 2월 22일 사이에 일본국 석천현 칠미시 연안 정치망어장에서 소나를 이용하여 실시하였고, 소나의 영상기록을 해석하여 얻어진 결과를 요약하면 다음과 같다. 1. 정치망 바깥쪽 부근에서 이동중인 어군의 행동양식은 어군의 각 위치에 따라 다른 행동이 관찰되었고, 부분적인 확대와 축소를 보이면서 어군전체가 이동하였다. 2. 헛통입구로 들어간 어군은 등망방향, 헛통의 등방향, 헛통의 옆줄방향으로 분산되었으며, 그후 헛통의 등방향, 헛통의 옆줄방향, 입구방향의 어군은 등망쪽으로 조밀하게 이동하는 행동양식을 취하면서 입구로부터 외등망까지 이동하였다. 3. 정치망의 안쪽과 바깥쪽에 있어서 어군의 주연부가 확대하는 경우, 최대 이동속도는 바깥쪽에서 277cm/sec, 안쪽에서 176cm/sec이었다.

대형정치망내에 있어서 어군의 행동 조사는 1992년 1월 29일부터 2월 22일 사이에 일본국 석천현 연안 정치망 어장에서 실시하고, 정치망어구의 어획기능에 관해서 해석, 검토하였으며, 얻어진 결과를 요약하면 다음과 같다. 1. 헛통에 출현한 어군수는 17~18시의 일몰시에 가장 많았고, 그후 급격히 감소하는 경향이었다, 이것으로 보아, 헛통에 있어서 어군의 체류시간은 짧고, 비교적 빠르게 원통에 입망했던가 혹은 헛통의 입구로부터 도피했다고 생각된다. 2. 망내에 입망한 정어리소형군의 행동은 측망에 평행한 직선적인 주복이동이 많고, 등망의 부근에서는 방향을 전환하여 헛통의 옆줄에 향하는 행동이 관찰되었다. 3. 망내에 입망한 정어리소형군의 이동속도는 25cm/sec가 최빈치이고, 최대 이동속도는 80cm/sec 이었다.

This research is a case study of underwater object tracking based on real-time recurrent regression networks (Re3). Re3 has the concept of generic object tracking. Because of these characteristics, it is very effective to apply this model to unclear underwater sonar images. The model also an pursues object tracking method, thus it solves the problem of calculating load that may be limited when object detection models are used, unlike the tracking models. The model is also highly intuitive, so it has excellent continuity of tracking even if the object being tracked temporarily becomes partially occluded or faded. There are 4 types of the dataset using multi-beam sonar images: including (a) dummy object floated at the testbed; (b) dummy object settled at the bottom of the sea; (c) tire object settled at the bottom of the testbed; (d) multi-objects settled at the bottom of the testbed. For this study, the experiments were conducted to obtain underwater sonar images from the sea and underwater testbed, and the validity of using noisy underwater sonar images was tested to be able to track objects robustly.

In the ground environment, mobile robot research uses sensors such as GPS and optical cameras to localize surrounding landmarks and to estimate the position of the robot. However, an underwater environment restricts the use of sensors such as optical cameras and GPS. Also, unlike the ground environment, it is difficult to make a continuous observation of landmarks for location estimation. So, in underwater research, artificial markers are installed to generate a strong and lasting landmark. When artificial markers are acquired with an underwater sonar sensor, different types of noise are caused in the underwater sonar image. This noise is one of the factors that reduces object detection performance. This paper aims to improve object detection performance through distortion and rotation augmentation of training data. Object detection is detected using a Faster R-CNN.

This study focuses on autonomous exploration based on map expansion for an underwater robot equipped with acoustic sonars. Map expansion is applicable to large-area mapping, but it may affect localization accuracy. Thus, as the key contribution of this paper, we propose a method for underwater autonomous exploration wherein the robot determines the trade-off between map expansion ratio and position accuracy, selects which of the two has higher priority, and then moves to a mission step. An occupancy grid map is synthesized by utilizing the measurements of an acoustic range sonar that determines the probability of occupancy. This information is then used to determine a path to the frontier, which becomes the new search point. During area searching and map building, the robot revisits artificial landmarks to improve its position accuracy as based on imaging sonar-based recognition and EKF-SLAM if the position accuracy is above the predetermined threshold. Additionally, real-time experiments were conducted by using an underwater robot, yShark, to validate the proposed method, and the analysis of the results is discussed herein.

Side scanning sonar (SSS) provides valuable information for robot navigation. However using the side scanning sonar images in the navigation was not fully studied. In this paper, we use range data, and side scanning sonar images from UnderWater Simulator (UWSim) and propose measurement models in a feature based simultaneous localization and mapping (SLAM) framework. The range data is obtained by echosounder and sidescanning sonar images from side scan sonar module for UWSim. For the feature, we used the A-KAZE feature for the SSS image matching and adjusting the relative robot pose by SSS bundle adjustment (BA) with Ceres solver. We use BA for the loop closure constraint of pose-graph SLAM. We used the Incremental Smoothing and Mapping (iSAM) to optimize the graph. The optimized trajectory was compared against the dead reckoning (DR).

기존의 예인식으로 운용하는 사이드스캔소나의 경우 조사 시 사용자가 원하는 단면 조사에 어려움이 있어 수중구조물 전동 지그가 개발되었지만, 전동지그를 사용한 음파촬영방법의 경우 보트와 소나가 일체 거동하기 때문에 파랑에 의한 보트 롤링현상, 보트운전자의 운용미 숙 등으로 인한 음파영상의 흔들림발생 등 , 작업환경에 따른 여러 가지 문제로 인하여 데이터의 왜곡이 발생하였다. 따라서 본 연구에서는 기존 사이드스캔소나의 수중조사를 위한 장비운용 시 발생하는 영상의 흔들림을 해결하기 위해, 흔들림 보정센서를 부착하고 흔들림 보정 알고리즘 을 개발하여 흔들림보정이 가능하도록 프로그램을 보완하였다. 또한, 소나 데이터 해상도의 향상 정도를 검증하기 위하여, 현장조사를 통해 흔 들림 보정 전‧ 후 음파영상을 수집하고 손상 의심부에 잠수사를 투입하여 실제 손상길이 및 수심을 측정하여 취득한 음파영상 데이터의 분석을 수행하였다. 본 연구는 향후 사이드스캔소나를 활용한 구조물의 수중부 및 하상면 음파영상 촬영기법의 발전에 기여할 것으로 판단된다.

In this paper we present (1) analysis of imaging sonar measurement for two-view relative pose estimation of an autonomous vehicle and (2) bundle adjustment and 3D reconstruction method using imaging sonar. Sonar has been a popular sensor for underwater application due to its robustness to water turbidity and visibility in water medium. While vision based motion estimation has been applied to many ground vehicles for motion estimation and 3D reconstruction, imaging sonar addresses challenges in relative sensor frame motion. We focus on the fact that the sonar measurement inherently poses ambiguity in its measurement. This paper illustrates the source of the ambiguity in sonar measurements and summarizes assumptions for sonar based robot navigation. For validation, we synthetically generated underwater seafloor with varying complexity to analyze the error in the motion estimation.

There are currently using widly and universally underwater inspection of based on SONAR around world. Understand the principles od SONAR’s ultrasonic for disaster prevention of underwater structures and though indoor test it could try consideration with respect to the characteristics of the ultrasonic frequency.

A filming technique using Side Scan SONAR should be varied upon condition in order to obtain high-resolution data. Because the theoretical measurement scope is different from the actual measurement scope. It is possible to obtain the accurate data only after adjusting the water depth, distance from a structure and emission angle. Via the multiple regression analysis of data accumulated through field experiments and relations of 3 variables, and equation was devised.

This paper proposes an underwater localization algorithm using probabilistic object recognition. It is organized as follows; 1) recognizing artificial objects using imaging sonar, and 2) localizing the recognized objects and the vehicle using EKF(Extended Kalman Filter) based SLAM. For this purpose, we develop artificial landmarks to be recognized even under the unstable sonar images induced by noise. Moreover, a probabilistic recognition framework is proposed. In this way, the distance and bearing of the recognized artificial landmarks are acquired to perform the localization of the underwater vehicle. Using the recognized objects, EKF-based SLAM is carried out and results in a path of the underwater vehicle and the location of landmarks. The proposed localization algorithm is verified by experiments in a basin.

Flood and scour are one of the major causes of bridge collapse. However, maintenance and inspection techniques still need improvements especially for underwater structures like bridge piers. Conventionally, manual inspection by divers has been carried out for underwater structures of bridges. This has difficulties in gathering accurate and reliable field data under severe environmental conditions. For this reason, underwater inspection is carried out using Side Scan Sonar(SSS) recently. The underwater structure image can be obtained by observing the reflected beam signal from underwater riverbed and surface. Signal and image processing techniques are employed in order to produce a clearer 2D image. Conventional inspection using one Side Scan Sonar is carried out to obtain river bed and underwater structure image. In order to reduce the inspection time, two Side Scan Sonar are used to obtain river bed and underwater structure image simultaneously. Two Side Scan Sonar were used to confirm the usability of the interference effects, which can occur by using the same frequency as the image at the time of collection, through indoor experiment for verification.

To contribute to efficient and safe inspection and maintenance of underwater facilities by identifying the damage status of underwater parts through collecting and analyzing local and overseas data. The carrying out actual underwater surveys to perform characteristics analysis and evaluation on the underwater facilities and present a Application Method of 3-D side scan sonar for Assessment of Underwater Structures.

This study introduces an algorithm for geometric distortion corrections in sonar images. The proposed algorithm mainly consists of two stages. At the first stage, the raw images are processed with median filter and Frost filter for noise reduction and intensity enhancement. In the last stage, the geometric distortion correction is conducted using angular information given by a gyro sensor. The algorithm was successfully applied on raw sonar data collected on a pier survey.

Damages due to typhoon and floods are increasing as a result of global warming and abnormal climate phenomenon over the world. Flood and scour is one of the major causes for bridge collapse. However, maintenance and inspection techniques still need improvements especially for underwater structures like bridge piers in rivers. Conventionally, manual inspection by divers has been carried out for underwater structures of bridges. This has difficulties in gathering accurate and reliable field data under severe environmental conditions. Therefore, in this study, underwater inspection of bridges has been carried out using the side scan sonar (SSS), one of unmanned underwater inspection equipment. Compared to the conventional manual inspection, it has been found that the SSS can be efficiently utilized to underwater inspection of bridges especially with bad visibility or large-scale inspection area.

This paper presents a method of sonar grid map matching for topological place recognition. The proposed method provides an effective rotation invariant grid map matching method. A template grid map is firstly extracted for reliable grid map matching by filtering noisy data in local grid map. Using the template grid map, the rotation invariant grid map matching is performed by Ring Projection Transformation. The rotation invariant grid map matching selects candidate locations which are regarded as representative point for each node. Then, the topological place recognition is achieved by calculating matching probability based on the candidate location. The matching probability is acquired by using both rotation invariant grid map matching and the matching of distance and angle vectors. The proposed method can provide a successful matching even under rotation changes between grid maps. Moreover, the matching probability gives a reliable result for topological place recognition. The performance of the proposed method is verified by experimental results in a real home environment

This paper presents a method of topological modeling using only low-cost sonar sensors. The proposed method constructs a topological model by extracting sub-regions from the local grid map. The extracted sub-regions are considered as nodes in the topological model, and the corresponding edges are generated according to the connectivity between two sub-regions. A grid confidence for each occupied grid is evaluated to obtain reliable regions in the local grid map by filtering out noisy data. Moreover, a convexity measure is used to extract sub-regions automatically. Through these processes, the topological model is constructed without predefining the number of sub-regions in advance and the proposed method guarantees the convexity of extracted sub-regions. Unlike previous topological modeling methods which are appropriate to the corridor-like environment, the proposed method can give a reliable topological modeling in a home environment even under the noisy sonar data. The performance of the proposed method is verified by experimental results in a real home environment.