An unmanned aerial vehicle （UAV） is arranged to explore an unknown environment and to map the features it finds when GPS is denied.It navigates using a statistical estimation technique known as simultaneous localiza...An unmanned aerial vehicle （UAV） is arranged to explore an unknown environment and to map the features it finds when GPS is denied.It navigates using a statistical estimation technique known as simultaneous localization and mapping （SLAM） which allows for the simultaneous estimation of the location of the UAV as well as the location of the features it sees.Obscrvability is a key aspect of the state estimation problem of SLAM.However,the dimension and variables of SLAM system might be changed with new features.To solve this issue,a unified approach of observability analysis for SLAM system is provided,through reorganizing the system model.The dimension and variables of SLAM system keep steady,then the PWCS theory can be used to analyze the local or total observability,and under special maneuver,some system states,such as the yaw angle,become observable.Simulation results validate the proposed method.展开更多
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. ...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 ＆ Hjj-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.展开更多
基金Project(2020203)supported by the Weapon Research Foundation,China
文摘An unmanned aerial vehicle （UAV） is arranged to explore an unknown environment and to map the features it finds when GPS is denied.It navigates using a statistical estimation technique known as simultaneous localization and mapping （SLAM） which allows for the simultaneous estimation of the location of the UAV as well as the location of the features it sees.Obscrvability is a key aspect of the state estimation problem of SLAM.However,the dimension and variables of SLAM system might be changed with new features.To solve this issue,a unified approach of observability analysis for SLAM system is provided,through reorganizing the system model.The dimension and variables of SLAM system keep steady,then the PWCS theory can be used to analyze the local or total observability,and under special maneuver,some system states,such as the yaw angle,become observable.Simulation results validate the proposed method.
基金Project(ZR2011FM005)supported by the Natural Science Foundation of Shandong Province,China
文摘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 ＆ Hjj-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.
