An Adaptive UKF Based SLAM Method for Unmanned Underwater Vehicle

Abstract EN

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.