A three-dimensional positioning method for global positioning system(GPS)receivers based on three satellites was proposed.In the method,the measurement equation used for positioning calculation was expanded by means o...A three-dimensional positioning method for global positioning system(GPS)receivers based on three satellites was proposed.In the method,the measurement equation used for positioning calculation was expanded by means of two measures.In this case,the measurement equation could be solved,and the function of positioning calculation could be performed.The detailed steps of the method and how to evaluate the positioning precision of the method were given,respectively.The positioning performance of the method was demonstrated through some experiments.It is shown that the method can provide the three-dimensional positioning information under the condition that there are only three useful satellites.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 (ZYGX2010J119)supported by the Fundamental Research Funds for the Central Universities of China
文摘A three-dimensional positioning method for global positioning system(GPS)receivers based on three satellites was proposed.In the method,the measurement equation used for positioning calculation was expanded by means of two measures.In this case,the measurement equation could be solved,and the function of positioning calculation could be performed.The detailed steps of the method and how to evaluate the positioning precision of the method were given,respectively.The positioning performance of the method was demonstrated through some experiments.It is shown that the method can provide the three-dimensional positioning information under the condition that there are only three useful satellites.
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.
