경위선이 비교적 넓은 간격으로 표시된 종이지도(이하 지도)에서 임의 지점의 경위도 좌표 값을 파악하는 것은 매우 불편하다. 따라서 휴대용GPS 단말기(이하 휴대용GPS)에 표시된 현재 위치의 경위도 좌표 값으로 지도상에서 그 위치를 파악하는 것도 역시 불편하다. 이러한 문제들로 풍부한 지리정보가 담겨져 있는 지도임에도 불구하고 휴대용GPS와 병용하는 것은 결코 쉽지 않다. 더욱 지도상에 다수의 좌표선과 해당 좌표 값 및 측지계가 표시되어 있지 않으면 휴대용GPS와 함께 사용하는 것은 불가능에 가깝다. 본 연구에서는 지도기반 평면격자좌표계란 새로운 기법을 이용하여 어떠한 형태의 지도라도 휴대용GPS와 편리하게 병용할 수 있는 방법을 모색해보고자 한다. 지도기반 평면격자좌표계(Map Based Rectangular Grid, 이하 평면격자좌표계)란 직접 지도에 임의로 기획하는 좌표계로서 축척에 관계없이 지도의 가로･세로 방향으로 1cm 간격마다 한 단위씩 수치가 증가하도록 구성한 좌표계이다. 휴대용GPS의 좌표계 설정에서 사용자가 직접 좌표계 속성을 입력할 수 있는 기능을 통해 임의의 위치 좌표 값을 지도에 기획한 평면격자좌표 값으로 표시할 수 있으므로 보다 편리하고 신속하게 지도상에서 임의의 위치 파악이 가능해진다. 또한 이 좌표계의 특성상 지도상에서 특정 지점의 평면격자좌표 값을 편리하고 신속하게 파악할 수 있을 뿐만 아니라 해당 수치를 휴대용GPS에 그대로 입력하여 특정지점(Waypoint)으로 저장할 수도 있다. 하지만 사용되는 지도에 따라 휴대용GPS에서 다소 복잡한 좌표계 설정을 재시도해야 하고, 취득한 평면격자좌표의 값은 상대적 좌표 값이라는 단점을 지니고 있다.

Recent studies on automatic parking have actively adopted the technology developed for mobile robots. Among them, the path planning scheme plans a route for a vehicle to reach a target parking position while satisfying the kinematic constraints of the vehicle. However, previous methods require a large amount of computation and/or cannot be easily applied to different environmental conditions. Therefore, there is a need for a path planning scheme that is fast, efficient, and versatile. In this study, we use a multi-dimensional path grid map to solve the above problem. This multi-dimensional path grid map contains a route which has taken a vehicle's kinematic constraints into account; it can be used with the A* algorithm to plan an efficient path. The proposed method was verified using Prescan which is a simulation program based on MATLAB. It is shown that the proposed scheme can successfully be applied to both parallel and vertical parking in an efficient manner.

본 연구의 목적은 이중편파 레이더 강우자료와 격자기반 분포형 강우-유출 모형인 KIMSTORM (KIneMatic wave STOrm Runoff Model)을 이용하여 유출해석을 수행하고, 침수실적자료와의 비교를 통해 레이더 강우자료의 효용성을 검토하는데 있다. 남강댐 유역(2,293 km²)을 대상으로, 2012년 4개의 강우 이벤트(집중호우, 카눈, 볼라벤, 산바)에 대하여 비슬산 레이더 강우자료를 사용하였다. 분포형 모형은 28개 지점 강우와 레이더 강우를 이용하여 보정되었으며, R² (coefficient of determination), ME(model efficiency), VCI (volume conservation index)를 이용하여 적용성을 평가하였다. 모형의 보정결과, R², ME, VCI의 평균이 지점강우 를 이용한 경우 각각 0.85, 0.78, 1.09, 레이더 강우를 이용한 경우 각각 0.85, 0.78, 0.96의 결과를 보였다. 태풍 산바에 의한 하천범람 침수실적자료의 두 침수지역(신연지구와 문대/신기지구)과 레이더와 지상강우에 의한 유출분석 결과를 비교하였다. 신연지구와 문대/신기지구 두 침수지역에서 레이더강우가 지상강우보다 더 많은 지역강우를 발생시켜 지표유출량을 더 크게 모의하는 것을 확인할 수 있었다. 특히 수위관측소가 존재하는 문대/신기지구의 경우, 지점강우보다 레이더 강우가 침수지역내 수위관측소의 실제 첨두유량에 가깝게 모의하였으며, 하천수위도 0.72 m 높게 모의하였다.

Mapping is a fundamental element for robotic services. There are available many types of map data representation such as grid map, metric map, topology map, etc. As more robots are deployed for services, more chances of exchanging map data among the robots emerge and standardization of map data representation (MDR) becomes more valuable. Currently, activities in developing MDR standard are underway in IEEE Robotics and Automation Society. The MDR standard is for a common representation and encoding of the two-dimensional map data used for navigation by mobile robots. The standard focuses on interchange of map data among components and systems, particularly those that may be supplied by different vendors. This paper aims to introduce MDR standard and its application to map merging. We have applied the basic structure of the MDR standard to a grid map and Voronoi graph as a kind of topology map and performed map merging between two different maps. Simulation results show that the proposed MDR is suitable for map data exchange among robots.

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.

Low-cost sensors have been widely used for mobile robot navigation in recent years. However, navigation performance based on low-cost sensors is not good enough to be practically used. Among many navigation techniques, building of an accurate map is a fundamental task for service robots, and mapping with low-cost IR sensors was investigated in this research. The robot’s orientation uncertainty was considered for mapping by modifying the Bayesian update formula. Then, the data association scheme was investigated to improve the quality of a built map when the robot’s pose uncertainty was large. Six low-cost IR sensors mounted on the robot could not give rich data enough to align the range data by the scan matching method, so a new sample-based method was proposed for data association. The real experiments indicated that the mapping method proposed in this research was able to generate a useful map for navigation.

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.