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    국내학술지 제목 게시판 내용
    제목(국문) SLAM 기술의 과거와 현재
    제목(영문) Past and State-of-the-Art SLAM Technologies
    저자 송재복 (Jae-Bok Song ,고려대학교 기계공학부 ) ▷공저자네트워크등록하기
    황서연 (Seo-Yeon Hwang ,고려대학교 기계공학부 ) ▷공저자네트워크등록하기
    초록
    초록(영문)
    This paper surveys past and state-of-the-art SLAM technologies. The standard methods for solving the SLAM problem are the Kalman filter, particle filter, graph, and bundle adjustment-based methods. Kalman filters such as EKF (Extended Kalman Filter) and UKF (Unscented Kalman Filter) have provided successful results for estimating the state of nonlinear systems and integrating various sensor information. However, traditional EKF-based methods suffer from the increase of computation burden as the number of features increases. To cope with this problem, particle filter-based SLAM approaches such as FastSLAM have been widely used. While particle filter-based methods can deal with a large number of features, the computation time still increases as the map grows. Graph-based SLAM methods have recently received considerable attention, and they can provide successful real-time SLAM results in large urban environments.
    keyword SLAM, Kalman filter, particle filter, GraphSLAM, bundle adjustment, mobile robot
    저널명 제어로봇시스템학회논문지 ▷관련저널보기
    VOL 20
    PAGE 372-379
    발표년도 2014
    국문File 국문다운로드
    영문File
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