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An Adaptive UKF Based SLAM Method for Unmanned Underwater Vehicle.
- Source :
-
Mathematical Problems in Engineering . 2013, p1-12. 12p. - Publication Year :
- 2013
-
Abstract
- This work proposes an improved unscented Kalman filter (UKF)-based simultaneous localization andmapping (SLAM) algorithm based on an adaptive unscented Kalman filter (AUKF) with a noise statistic estimator. The algorithm solves the issue that conventional UKF-SLAMalgorithms 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 matchingmethod. 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1024123X
- Database :
- Academic Search Index
- Journal :
- Mathematical Problems in Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 94813535
- Full Text :
- https://doi.org/10.1155/2013/605981