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이무현,허수정,박용완,Lee, Moohyun,Hur, Soojung,Park, Yongwan 대한임베디드공학회 2015 대한임베디드공학회논문지 Vol.10 No.3
We propose obstacle classification method based on 2D LIDAR(Light Detecting and Ranging) database. The existing obstacle classification method based on 2D LIDAR, has an advantage in terms of accuracy and shorter calculation time. However, it was difficult to classifier the type of obstacle and therefore accurate path planning was not possible. In order to overcome this problem, a method of classifying obstacle type based on width data of obstacle was proposed. However, width data was not sufficient to improve accuracy. In this paper, database was established by width, intensity, variance of range, variance of intensity data. The first classification was processed by the width data, and the second classification was processed by the intensity data, and the third classification was processed by the variance of range, intensity data. The classification was processed by comparing to database, and the result of obstacle classification was determined by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that calculation time declined in comparison to 3D LIDAR and it was possible to classify obstacle using single 2D LIDAR.
단일 2차원 라이다 기반의 다중 특징 비교를 이용한 장애물 분류 기법
이무현(Moohyun Lee),허수정(Soojung Hur),박용완(Yongwan Park) 제어로봇시스템학회 2016 제어·로봇·시스템학회 논문지 Vol.22 No.4
We propose an obstacle classification method using multi-decision factors and decision sections based on Single 2D LiDAR. The existing obstacle classification method based on single 2D LiDAR has two specific advantages: accuracy and decreased calculation time. However, it was difficult to classify obstacle type, and therefore accurate path planning was not possible. To overcome this problem, a method of classifying obstacle type based on width data was proposed. However, width data was not sufficient to enable accurate obstacle classification. The proposed algorithm of this paper involves the comparison between decision factor and decision section to classify obstacle type. Decision factor and decision section was determined using width, standard deviation of distance, average normalized intensity, and standard deviation of normalized intensity data. Experiments using a real autonomous vehicle in a real environment showed that calculation time decreased in comparison with 2D LiDAR-based method, thus demonstrating the possibility of obstacle type classification using single 2D LiDAR.
김재현(Jaehyun Kim),김성표(Sungpyo Kim),이무현(Moohyun Lee),김문철(Munchurl Kim) 한국방송·미디어공학회 2024 한국방송미디어공학회 학술발표대회 논문집 Vol.2024 No.6
확산 모델은(Diffusion Model) 이미지 합성 분야에서 최첨단 성능을 보여주었다. 이 모델은 빠르게 발전하여 이미지 합성분야 뿐만 아니라 초해상화 연구에도 적용되고 있다. 초해상화 연구에서 확산모델의 적용이 활발히 연구되고 있지만 inference 시의 반복적인 디노이징(denoising) 과정과 어텐션(attention)기반의 모듈로 높은 GPU 메모리 사용량이 요구된다. 본 연구는 이 문제를 해결하기 위해 어텐션(attention )모듈을 사용하지 않는 효율적인 확산-초해상화 모델을 제안한다.