A novel robust approach for SLAM of mobile robot
来源期刊：中南大学学报(英文版)2014年第6期
论文作者：MA Jia-chen(马家辰) ZHANG Qi(张琦) MA Li-yong(马立勇)
文章页码：2208 - 2215
Key words：mobile robot; simultaneous localization and mapping (SLAM); improved FastSLAM 2.0; H∞ filter; particle swarm optimization (PSO)
Abstract: The task of simultaneous localization and mapping (SLAM) is to build environmental map and locate the position of mobile robot at the same time. FastSLAM 2.0 is one of powerful techniques to solve the SLAM problem. However, there are two obvious limitations in FastSLAM 2.0, one is the linear approximations of nonlinear functions which would cause the filter inconsistent and the other is the “particle depletion” phenomenon. A kind of PSO & H∞-based FastSLAM 2.0 algorithm is proposed. For maintaining the estimation accuracy, H∞ filter is used instead of EKF for overcoming the inaccuracy caused by the linear approximations of nonlinear functions. The unreasonable proposal distribution of particle greatly influences the pose state estimation of robot. A new sampling strategy based on PSO (particle swarm optimization) is presented to solve the “particle depletion” phenomenon and improve the accuracy of pose state estimation. The proposed approach overcomes the obvious drawbacks of standard FastSLAM 2.0 algorithm and enhances the robustness and efficiency in the parts of consistency of filter and accuracy of state estimation in SLAM. Simulation results demonstrate the superiority of the proposed approach.
MA Jia-chen(马家辰)1, 2, ZHANG Qi(张琦)1 , MA Li-yong(马立勇)1
(1. School of Astronautics, Harbin Institute of Technology, Harbin 150001, China;
2. School of Informations and Electrical Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China)
Abstract:The task of simultaneous localization and mapping (SLAM) is to build environmental map and locate the position of mobile robot at the same time. FastSLAM 2.0 is one of powerful techniques to solve the SLAM problem. However, there are two obvious limitations in FastSLAM 2.0, one is the linear approximations of nonlinear functions which would cause the filter inconsistent and the other is the “particle depletion” phenomenon. A kind of PSO & H∞-based FastSLAM 2.0 algorithm is proposed. For maintaining the estimation accuracy, H∞ filter is used instead of EKF for overcoming the inaccuracy caused by the linear approximations of nonlinear functions. The unreasonable proposal distribution of particle greatly influences the pose state estimation of robot. A new sampling strategy based on PSO (particle swarm optimization) is presented to solve the “particle depletion” phenomenon and improve the accuracy of pose state estimation. The proposed approach overcomes the obvious drawbacks of standard FastSLAM 2.0 algorithm and enhances the robustness and efficiency in the parts of consistency of filter and accuracy of state estimation in SLAM. Simulation results demonstrate the superiority of the proposed approach.
Key words:mobile robot; simultaneous localization and mapping (SLAM); improved FastSLAM 2.0; H∞ filter; particle swarm optimization (PSO)
