Details

Autor(en) / Beteiligte

• Wang, Hongjian
• Fu, Guixia
• Li, Juan
• Yan, Zheping
• Bian, Xinqian
• Mikhlin, Yuri Vladimirovich

Titel

An Adaptive UKF Based SLAM Method for Unmanned Underwater Vehicle

Ist Teil von

• Mathematical problems in engineering, 2013-01, Vol.2013, p.1-12

Ort / Verlag

New York: Hindawi Publishing Corporation

Erscheinungsjahr

2013

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.