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학술대회 Robust Regression to Varying Data Distribution and Its Application to Landmark-based Localization
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저자
최성록, 김종환
발행일
200810
출처
International Conference on Systems, Man and Cybernetics (SMC) 2008, pp.3465-3470
DOI
https://dx.doi.org/10.1109/ICSMC.2008.4811834
협약과제
08MC5400, u-Robot 인지인프라 기술개발(주관 : u-City 환경기반 하이브리드 u-로봇 서비스 시스템 기술개발), 유원필
초록
Data may be wrongly measured or come from other sources. Such data is a big problem in regression, which retrieve parameters from data. Random Sample Consensus (RANSAC) and Maximum Likelihood Estimation Sample Consensus (MLE-SAC) are representative researches, which focused on this problem. However, they do not cope with varying data distribution because they need to tune variables according to given data. This paper proposes user-independent parameter estimator, u-MLESAC, which is based on MLESAC. It estimates variables necessary in probabilistic error model through expectation maximization (EM). It also terminates adaptively using failure rate and error tolerance, which can control trade-off between accuracy and running time. Line fitting experiments showed its high accuracy and robustness in varying data distribution. Its results are compared with other estimators. Its application to landmark-based localization also verified its performance compared with other estimator. © 2008 IEEE.
KSP 제안 키워드
Data Distribution, Error tolerance, Failure Rate, High accuracy, Parameter estimator, Random sample consensus, Robust regression, Running time, Trade-off, error model, expectation maximization