최근 태국의 수도 방콕의 장기화된 홍수로 인하여 인명 및 국가 기간시설물에 대한 피해가 발생하였고, 전 세계적으로도 지구온난화 및 이상기후 현상으로 태풍 및 홍수에 대한 피해가 증가하고 있는 추세이다. 이로 인해 국내외에서 해마다 홍수로 인하여 많은 교량들이 붕괴되고 있으며, 교량파괴의 주요 원인은 홍수와 세굴인 것으로 분석되었다. 교량에서 중요한 부분인 교각처럼 수중에 위치한 구조물에 대해서는 결함을 찾아내고 이를 유지 관리하는 제반 기술적 사항이 미흡한 실정이다. 또한 교각수중점검을 수행하는 용역방식이 외부 비전문적인 용역방식과 인력 위주의 점검으로 인하여 안전관리 부재 및 점검자의 주관성 개입 등 데이터 획득과 결함 평가의 신뢰성에 나쁜 영향을 미치고, 형식적인 점검이 이루어지고 있는 실정이다. 따라서, 본 연구에서는 교량의 수중구조물점검을 위해 무인 수중 점검 장비 중 하나인 수중음파탐지기(Side Scan Sonar : SSS)를 이용한 실험으로 무인점검의 가능성에 대해 조사하였고 사용성 검증을 위한 수중촬영도 함께 실시하였다. 실험결과를 통해 Sonar를 이용한 교량 수중구조물 점검이 탁도가 불량이거나 대규모 교량의 세굴 탐지에 있어 효율적이라고 판단된다.

It is very important for a mobile robot to recognize and model its environments for navigation. However, the grid map constructed by sonar sensors cannot accurately represent the environment, especially the narrow environment, due to the angular uncertainty of sonar data. Therefore, we propose a map building scheme which combines sonar sensors and IR sensors. The maps built by sonar sensors and IR sensors are combined with different weights which are determined by the degree of translational and rotational motion of a robot. To increase the effectiveness of sensor fusion, we also propose optimal sensor arrangement through various experiments. The experimental results show that the proposed method can represent the environment such as narrow corridor and open door more accurately than conventional sonar sensor-based map building methods.

Representing an environment as the probabilistic grids is effective to sense outlines of the environment in the mobile robot area. Outlines of an environment can be expressed factually by using the probabilistic grids especially if sonar sensors would be supposed to build an environment map. However, the difficult problem of a sonar such as a specular reflection phenomenon should be overcome to build a grid map through sonar observations. In this paper, the NRF(Neighborhood Recognition Factor) was developed for building a grid map in which the specular reflection effect is minimized. Also the reproduction rate of the gird map built by using NRF was analyzed with respect to a true map. The experiment was conducted in a home environment to verify the proposed technique.

Improving practicality of SLAM requires various sensors to be fused effectively in order to cope with uncertainty induced from both environment and sensors. In this case, combining sonar and vision sensors possesses numerous advantages of economical efficiency and complementary cooperation. Especially, it can remedy false data association and divergence problem of sonar sensors, and overcome low frequency SLAM update caused by computational burden and weakness in illumination changes of vision sensors. In this paper, we propose a SLAM method to join sonar sensors and stereo camera together. It consists of two schemes, extracting robust point and line features from sonar data and recognizing planar visual objects using multi-scale Harris corner detector and its SIFT descriptor from pre-constructed object database. And fusing sonar features and visual objects through EKF-SLAM can give correct data association via object recognition and high frequency update via sonar features. As a result, it can increase robustness and accuracy of SLAM in indoor environment. The performance of the proposed algorithm was verified by experiments in home –like environment.

Due to the growth of population and industrial development at the coastal cities, there has been much increase in necessity to effective control of the wastes into the coastal water and river. The amount of disposal at those waters has been increased rapidly ana it is necessary for us to track of it in order to keep the waterway safe and the water clean. The investigation and research in terms of water quality in these regions have been conducted frequently but the systematic survey of the disposed wastes at the bottom was neglected and/or minor. In this study we surveyed the status of disposed waste distribution at the bottom of coastal water and river from the scanned images. The intensity of sound received by the side scan sonar tow fish from the sea floor provides information as to the general distribution and characteristics of the superficial wastes. The port and starboard side scanned images produced from two arrays of transducers borne on a tow fish connected by tow cable to a tug boat have the area with width of 22m~112m and band of 44m~224m. All data are displayed in real-time on a high-resolution color display (1280×1024 pixels) together with position information by DGPS. From the field measurement and analysis of the recorded images, we could draw the location and distribution of bottom disposals. Furthermore, we could make a database system which might be useful for navigation and fundamental for planning the waste reception and process control system.