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For the problems of estimation accuracy, inconsistencies and robustness in mobile robot simultaneous localization and mapping(SLAM), a novel SLAM based on improved Rao-Blackwellized H_∞ particle filter(IRBHF-SLAM) algorithm is proposed.The iterated unscented H_∞ filter(IUHF) is utilized to accurately calculate the importance density function, repeatedly correcting the state mean and the covariance matrix by the iterative update method.The laser sensor’s observation information is introduced into sequential importance sampling routine.It can avoid the calculation of Jacobian matrix and linearization error accumulation; meanwhile, the robustness of the algorithm is enhanced.IRBHF-SLAM is compared with Fast SLAM2.0 and the unscented Fast SLAM(UFast SLAM) under different noises in simulation experiments.Results show the algorithm can improve the estimation accuracy and stability.The improved approach, based on the robot operation system(ROS), runs on the Pioneer3-DX robot equipped with a HOKUYO URG-04LX(URG) laser range finder.Experimental results show the improved algorithm can reduce the required number of particles and the operating time; and create online 2 dimensional(2-D) grid-map with high precision in different environments.
For the problems of estimation accuracy, inconsistencies and robustness in mobile robot simultaneous localization and mapping (SLAM), a novel SLAM based on improved Rao-Blackwellized H_∞ particle filter (IRBHF-SLAM) algorithm is proposed. IUHF) is utilized to accurately calculate the importance density function, repeatedly correcting the state mean and the covariance matrix by the iterative update method. The laser sensor’s observation information is introduced into sequential importance sampling routine. It can avoid the calculation of Jacobian matrix and linearization error accumulation; meanwhile, the robustness of the algorithm is enhanced. IRBHF-SLAM is compared with Fast SLAM2.0 and the unscented Fast SLAM (UFast SLAM) under different noises in simulation experiments. Results show the algorithm can improve the estimation accuracy and stability The improved approach, based on the robot operation system (ROS), runs on the Pioneer 3-DX robot equipped with a HOKUYO URG-04LX (URG) laser range finder. Experimental results show the improved algorithm can reduce the required number of particles and the operating time; and create online 2 dimensional (2-D) grid-map with high precision in different environments.