Abstract: To alleviate the problems of low estimation accuracy,serious inconsistencies and poor robustness in mobile robot simultaneous localization and mapping(SLAM),a novel iterated unscented H∞ filter based SLAM algorithm was derived.The unscented transformation is introduced into the extended H∞filter to estimate the system state mean and covariance matrix,avoiding the derivation of Jacobian matrix and linearization error accumulation.Meanwhile,the numerical stability of the algorithm is enhanced.With the iterative update method,the observation information is utilized to repeatedly correct the state mean and the covariance matrix to further lower the estimation error.The proposed algorithm was compared with EKF-SLAM,UKF-SLAM and CEHF-SLAM under different environments and noises in simulation experiments.Results show that the proposed SLAM can still maintain the high estimation accuracy and robustness in different terrible noises,and adapts to different environments.The effectiveness and feasibility of the algorithm are verified.

Key words: Simultaneous localization and mapping,Iterated unscented H∞ filter,Robustness,Estimation accuracy