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• W2019821607 abstract "This work proposes an improved unscented Kalman filter (UKF)-based simultaneous localization and mapping (SLAM) algorithm based on an adaptive unscented Kalman filter (AUKF) with a noise statistic estimator. The algorithm solves the issue that conventional UKF-SLAM algorithms have declining accuracy, with divergence occurring when the prior noise statistic is unknown and time-varying. The new SLAM algorithm performs an online estimation of the statistical parameters of unknown system noise by introducing a modified Sage-Husa noise statistic estimator. The algorithm also judges whether the filter is divergent and restrains potential filtering divergence using a covariance matching method. This approach reduces state estimation error, effectively improving navigation accuracy of the SLAM system. A line feature extraction is implemented through a Hough transform based on the ranging sonar model. Test results based on unmanned underwater vehicle (UUV) sea trial data indicate that the proposed AUKF-SLAM algorithm is valid and feasible and provides better accuracy than the standard UKF-SLAM system." @default.
• W2019821607 created "2016-06-24" @default.
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• W2019821607 date "2013-01-01" @default.
• W2019821607 modified "2023-10-17" @default.
• W2019821607 title "An Adaptive UKF Based SLAM Method for Unmanned Underwater Vehicle" @default.
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• W2019821607 doi "https://doi.org/10.1155/2013/605981" @default.
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