An Adaptive UKF Based SLAM Method for Unmanned Underwater Vehicle. (6th November 2013)

• Record Type: Journal Article
• Authors: Wang, Hongjian; Fu, Guixia; Li, Juan; Yan, Zheping; Bian, Xinqian
• Other Names: Mikhlin Yuri Vladimirovich Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2013(2013)
• Publication Date: 2013-11-06
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2013/605981
• Languages: English
• ISSNs: 1024-123X

Abstract

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.