The choice of the particle's distribution model and the consistency of the result are very important for FastSLAM.The improved auxiliary variable model with FastSLAM,and Stirling Interpolation which is used to app...The choice of the particle's distribution model and the consistency of the result are very important for FastSLAM.The improved auxiliary variable model with FastSLAM,and Stirling Interpolation which is used to approximate the nonlinear functions are provided.This approach improves the precision of the approximation for the nonlinear functions,conquers the drawback of the FastSLAM1.0 by using a model ignoring the measurement data,enhances the estimation consistency of the robot pose,and reduces the degradation speed of the particle in FastSLAM algorithm.Simulation results demonstrate the excellence of the proposed algorithm and give the noise parameter influence on the proposed algorithm.展开更多
This paper presents new methods for spacecraft relative pose estimation using the Unscented Kalman Filter(UKF),taking into account non-additive process and measurement noises.A twistor model is employed to represent t...This paper presents new methods for spacecraft relative pose estimation using the Unscented Kalman Filter(UKF),taking into account non-additive process and measurement noises.A twistor model is employed to represent the spacecraft's relative 6-DOF motion of the chaser with respect to the target,expressed in the chaser body frame.The twistor model utilizes Modified Rodrigues Parameters(MRPs)to represent attitude with a minimal number of parameters,eliminating the need for the normalization constraint that exists in the quaternion-based model.Additionally,it incorporates both relative position and attitude in a single model,addressing kinematic coupling of states and simplifying the estimator design.Despite numerous existing pose estimation algorithms,many rely on the simplification of additive noise assumptions.This work enhances the robustness and improves the convergence of non-additive noise algorithms by deriving two methods to accurately approximate process and measurement noise covariance matrices for systems with non-additive noises.The first method utilizes the Stirling Interpolation Formula(SIF)to obtain equivalent process and measurement noise covariance matrices.The second method employs State Noise Compensation(SNC)to derive the equivalent process noise covariance matrix and uses SIF to compute the equivalent measurement noise covariance matrix.These methods are integrated into the UKF framework for estimating the relative pose of spacecraft in proximity operations,demonstrated through two scenarios:one with a cooperative target using Position Sensing Diodes(PSDs)and another with an uncooperative target using LiDAR for 3-D imaging.The effectiveness of these methods is validated against others in the literature through Monte Carlo simulations,showcasing their faster convergence and robust performance.展开更多
基金National High-Tech Research and Development Program of China(No.2003AA1Z2130)Science and Technology Project of Zhejiang Province,China(No.2005C11001-02)
文摘The choice of the particle's distribution model and the consistency of the result are very important for FastSLAM.The improved auxiliary variable model with FastSLAM,and Stirling Interpolation which is used to approximate the nonlinear functions are provided.This approach improves the precision of the approximation for the nonlinear functions,conquers the drawback of the FastSLAM1.0 by using a model ignoring the measurement data,enhances the estimation consistency of the robot pose,and reduces the degradation speed of the particle in FastSLAM algorithm.Simulation results demonstrate the excellence of the proposed algorithm and give the noise parameter influence on the proposed algorithm.
基金the startup and UPAR grants funded by College of Engineering at United Arab Emirates University(UAEU).The grant codes are G00003527 and G00004562.
文摘This paper presents new methods for spacecraft relative pose estimation using the Unscented Kalman Filter(UKF),taking into account non-additive process and measurement noises.A twistor model is employed to represent the spacecraft's relative 6-DOF motion of the chaser with respect to the target,expressed in the chaser body frame.The twistor model utilizes Modified Rodrigues Parameters(MRPs)to represent attitude with a minimal number of parameters,eliminating the need for the normalization constraint that exists in the quaternion-based model.Additionally,it incorporates both relative position and attitude in a single model,addressing kinematic coupling of states and simplifying the estimator design.Despite numerous existing pose estimation algorithms,many rely on the simplification of additive noise assumptions.This work enhances the robustness and improves the convergence of non-additive noise algorithms by deriving two methods to accurately approximate process and measurement noise covariance matrices for systems with non-additive noises.The first method utilizes the Stirling Interpolation Formula(SIF)to obtain equivalent process and measurement noise covariance matrices.The second method employs State Noise Compensation(SNC)to derive the equivalent process noise covariance matrix and uses SIF to compute the equivalent measurement noise covariance matrix.These methods are integrated into the UKF framework for estimating the relative pose of spacecraft in proximity operations,demonstrated through two scenarios:one with a cooperative target using Position Sensing Diodes(PSDs)and another with an uncooperative target using LiDAR for 3-D imaging.The effectiveness of these methods is validated against others in the literature through Monte Carlo simulations,showcasing their faster convergence and robust performance.
