We present an investigation into the use of pan tilt zoom camera and sonar sensors for simuhaneous localization and mapping with artificial colored landmarks. An improved particle filter is applied to estimate a poste...We present an investigation into the use of pan tilt zoom camera and sonar sensors for simuhaneous localization and mapping with artificial colored landmarks. An improved particle filter is applied to estimate a posterior of the pose of the robot, in which each particle has associated it with an entire map. The distributions of landmarks are also represented by particle sets, where separate particles are used to represent the robot and the landmarks. Hough transform is used to extract line segments from sonar observations and build map simultaneously. The key advantage of our method is that the full posterior over robot poses and landmarks can be nonlinearly approximated at every point in time by particles. Especially the landmarks are affixed on the moving robots, which can reduce the impact of the depletion problem and the impoverishment problem produced by basic particle filter. Experimental results show that this approach has advantages over the basic particle filter and the extended Kalman filter.展开更多
Land data assimilation(DA) has gradually developed into an important earth science research method because of its ability to combine model simulations and observations.Integrating new observations into a land surface ...Land data assimilation(DA) has gradually developed into an important earth science research method because of its ability to combine model simulations and observations.Integrating new observations into a land surface model by the DA method can correct the predicted trajectory of the model and thus,improve the accuracy of state variables.It can also reduce uncertainties in the model by estimating some model parameters simultaneously.Among the various DA methods,the particle filter is free from the constraints of linear models and Gaussian error distributions,and can be applicable to any nonlinear and non-Gaussian state-space model;therefore,its importance in land data assimilation research has increased.In this study,a DA scheme was developed based on the residual resampling particle filter.Microwave brightness temperatures were assimilated into the macro-scale semi-distributed variance infiltration capacity model to estimate the surface soil moisture and three hydraulic parameters simultaneously.Finally,to verify the scheme,a series of comparative experiments was performed with experimental data obtained during the Soil Moisture Experiment of 2004 in Arizona.The results show that the scheme can improve the accuracy of soil moisture estimations significantly.In addition,the three hydraulic parameters were also well estimated,demonstrating the effectiveness of the DA scheme.展开更多
文摘We present an investigation into the use of pan tilt zoom camera and sonar sensors for simuhaneous localization and mapping with artificial colored landmarks. An improved particle filter is applied to estimate a posterior of the pose of the robot, in which each particle has associated it with an entire map. The distributions of landmarks are also represented by particle sets, where separate particles are used to represent the robot and the landmarks. Hough transform is used to extract line segments from sonar observations and build map simultaneously. The key advantage of our method is that the full posterior over robot poses and landmarks can be nonlinearly approximated at every point in time by particles. Especially the landmarks are affixed on the moving robots, which can reduce the impact of the depletion problem and the impoverishment problem produced by basic particle filter. Experimental results show that this approach has advantages over the basic particle filter and the extended Kalman filter.
基金supported by the Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences under the project "High-resolution Optical Image Automatic Target Recognition"(Grant No.Y2YY02101B)
文摘Land data assimilation(DA) has gradually developed into an important earth science research method because of its ability to combine model simulations and observations.Integrating new observations into a land surface model by the DA method can correct the predicted trajectory of the model and thus,improve the accuracy of state variables.It can also reduce uncertainties in the model by estimating some model parameters simultaneously.Among the various DA methods,the particle filter is free from the constraints of linear models and Gaussian error distributions,and can be applicable to any nonlinear and non-Gaussian state-space model;therefore,its importance in land data assimilation research has increased.In this study,a DA scheme was developed based on the residual resampling particle filter.Microwave brightness temperatures were assimilated into the macro-scale semi-distributed variance infiltration capacity model to estimate the surface soil moisture and three hydraulic parameters simultaneously.Finally,to verify the scheme,a series of comparative experiments was performed with experimental data obtained during the Soil Moisture Experiment of 2004 in Arizona.The results show that the scheme can improve the accuracy of soil moisture estimations significantly.In addition,the three hydraulic parameters were also well estimated,demonstrating the effectiveness of the DA scheme.
