In this study, we present an algorithm for indoor robot position estimation. Estimating the position of an indoor robot using a fixed imaging device obviates the need for complex sensors or hardware, enabling easy estimation of absolute position through marker recognition. However, location estimation becomes impossible when the device moves away from the surrounding obstacles or the screen, presenting a significant drawback. To solve this problem, we propose an algorithm that improves the precision of robot indoor location estimation using a Gaussian Mixture Model(GMM) and a Kalman filter estimation model. We conducted an actual robot operation experiment and confirmed accurate position estimation, even when the robot was out of the image.

Recent developments in robotics and intelligent vehicle area, bring interests of people in an autonomous driving ability and advanced driving assistance system. Especially fully automatic parking ability is one of the key issues of intelligent vehicles, and accurate parked vehicles detection is essential for this issue. In previous researches, many types of sensors are used for detecting vehicles, 2D LiDAR is popular since it offers accurate range information without preprocessing. The L shape feature is most popular 2D feature for vehicle detection, however it has an ambiguity on different objects such as building, bushes and this occurs misdetection problem. Therefore we propose the accurate vehicle detection method by using a 3D complete vehicle model in 3D point clouds acquired from front inclined 2D LiDAR. The proposed method is decomposed into two steps: vehicle candidate extraction, vehicle detection. By combination of L shape feature and point clouds segmentation, we extract the objects which are highly related to vehicles and apply 3D model to detect vehicles accurately. The method guarantees high detection performance and gives plentiful information for autonomous parking. To evaluate the method, we use various parking situation in complex urban scene data. xperimental results shows the qualitative and quantitative performance efficiently.

게임에서 레벨 디자인 (Level Design)과 캐릭터간의 밸런스는 게임의 흥미를 결정하는 매우 중요한 요소이며, 레벨 디자이너에 의해 결정 된다. 기존의 게임에서는 플레이어가 가장 큰 재미를 느낄 수 있는 캐릭터의 공격 패턴과 속성은 정적으로 정해졌으며 스크립트 형식으로 표현됐다. 이와 같이 정적으로 정해진 레벨에 따라 진행되면 플레이어가 쉽게 적응하게 되고, 플레이어의 학습능력에 따라 레벨 디자이너가 의도했던 밸런스가 깨질 수 있었다. 이런 문제점을 해결하기 위해 본 논문에서는 게임 도중에 플레이어의 대응 패턴을 GMM(Gaussian Mixture Model)으로 모델링하고 분석하여 레벨 디자이너가 의도했던 레벨과 재미를 느낄 수 있는 환경을 제공하는 방법을 제안한다. 제안한 방법을 실제 2D슈팅게임에 적용하여 플레이어의 패턴을 분석한 결과와 동적 레벨 디자인의 결과를 보인다.