In this paper, the marginal Rao-Blackwellized particle filter （MRBPF）, which fuses the Rao-Blackwellized particle filter （RBPF） algorithm and the marginal particle filter （MPF） algorithm, is presented. The state...In this paper, the marginal Rao-Blackwellized particle filter （MRBPF）, which fuses the Rao-Blackwellized particle filter （RBPF） algorithm and the marginal particle filter （MPF） algorithm, is presented. The state space is divided into linear and non-linear parts, which can be estimated separately by the MPF and the optional Kalman filter. Through simulation in the terrain aided navigation （TAN） domain, it is demonstrated that, compared with the RBPF, the root mean square errors （RMSE） and the error variance of the nonlinear state estimations by the proposed MRBPF are respectively reduced by 29% and 96%, while the unique particle count is increased by 80%. It is also found that the MRBPF has better convergence properties, and analysis has shown that the existing RBPF is nothing more than a special case of the MRBPF.展开更多
This paper presents a modified Rao-Blackwellized Particle Filter (RBPF) approach for the bearing-only monocular SLAM problem. While FastSLAM 2.0 is known to be one of the most computationally efficient SLAM approaches...This paper presents a modified Rao-Blackwellized Particle Filter (RBPF) approach for the bearing-only monocular SLAM problem. While FastSLAM 2.0 is known to be one of the most computationally efficient SLAM approaches;it is not applicable to certain formulations of the SLAM problem in which some of the states are not explicitly expressed in the measurement equation. This constraint impacts the versatility of the FastSLAM 2.0 in dealing with partially ob-servable systems, especially in dynamic environments where inclusion of higher order but unobservable states such as velocity and acceleration in the filtering process is highly desirable. In this paper, the formulation of an enhanced RBPF-based SLAM with proper sampling and importance weights calculation for resampling distributions is presented. As an example, the new formulation uses the higher order states of the pose of a monocular camera to carry out SLAM for a mobile robot. The results of the experiments on the robot verify the improved performance of the higher order RBPF under low parallax angles conditions.展开更多
For the problems of estimation accuracy, inconsistencies and robustness in mobile robot simultaneous localization and mapping （SLAM）, a novel SLAM based on improved Rao-Blackwellized H∞ particle filter （IRBHF-SLAM...For the problems of estimation accuracy, inconsistencies and robustness in mobile robot simultaneous localization and mapping （SLAM）, a novel SLAM based on improved Rao-Blackwellized H∞ particle filter （IRBHF-SLAM） algorithm is proposed. The iterated unscented H∞ filter （IUHF） is utilized to accurately calculate the importance density function, repeatedly correcting the state mean and the covariance matrix by the iterative update method. The laser sensor＇s observation information is introduced into sequential importance sampling routine. It can avoid the calculation of Jacobian matrix and linearization error accumulation; meanwhile, the robustness of the algorithm is enhanced. IRBHF-SLAM is compared with FastSLAM2.0 and the unscented FastSLAM （UFastSLAM） under different noises in simulation experiments. Results show the algorithm can improve the estimation accuracy and stability. The improved approach, based on the robot operation system （ROS）, runs on the Pioneer3-DX robot equipped with a HOKUYO URG-04LX （URG） laser range finder. Experimental results show the improved algorithm can reduce the required number of particles and the operating time; and create online 2 dimensional （2-D） grid-map with high precision in different environments.展开更多
A probabilistic algorithm is proposed for the problem of simultaneous robot localization and peopletracking （SLAP） using single onboard sensor in situations with sensor noise and global uncertainties over the obser...A probabilistic algorithm is proposed for the problem of simultaneous robot localization and peopletracking （SLAP） using single onboard sensor in situations with sensor noise and global uncertainties over the observer＇s pose. By the decomposition of the joint distribution according to the Rao-Blackwell theorem, posteriors of the robot pose are sequentially estimated over time by a smoothed laser perception model and an improved resampling scheme with evolution strategies; the conditional distribution of the person＇s position is estimated using unscented Kalman filter （UKF） to deal with the nonlinear dynamic of human motion. Experiments conducted in a real indoor service robot scenario validate the favorable performance of the positional accuracy as well as the improved computational efficiency.展开更多
The Rao-Blackwellized Particle Filter （RBPF） is widely used for high dimensional nonlinear sys- tems, often with a linear Gaussian substructure. However, the RBPF is just a specific method in the class of Rao-Blackw...The Rao-Blackwellized Particle Filter （RBPF） is widely used for high dimensional nonlinear sys- tems, often with a linear Gaussian substructure. However, the RBPF is just a specific method in the class of Rao-Blackwellized Filtering （RBF）. This paper analyzes the recursive structure of the RBF from a more gen- eral perspective. The research starts from a general system model and studies the interconnected relation- ships between the two subspaces during the iterations. The results illustrate the working mechanisms of the RBF with an extensible framework for easily building Rao-Blackwellized algorithms with common nonlinear filters. Several examples are given to illustrate how to build new filters using this framework.展开更多
针对移动机器人同时定位与地图创建(Simultaneous localization and mapping,SLAM)中的FastSLAM算法,存在非线性系统线性化处理和计算雅可比矩阵的缺点,本文提出了基于Sterling多项式插值处理非线性系统的SLAM方法.该方法基于Rao-Blackw...针对移动机器人同时定位与地图创建(Simultaneous localization and mapping,SLAM)中的FastSLAM算法,存在非线性系统线性化处理和计算雅可比矩阵的缺点,本文提出了基于Sterling多项式插值处理非线性系统的SLAM方法.该方法基于Rao-Blackwellized粒了滤波框架,利用中心差分滤波方法产生改进的建议分布函数,提高了机器人位姿估计的精度;利用中心差分滤波初始化特征和更新地图中的特征,提高了地图创建的精度;针对实际应用中存在虚假特征的情况提出了一种有效的地图管理方法.在同等粒了数的情况下,该方法改进了SLAM结果的精度.基于仿真和实际数据的实验结果验证了该方法的有效性.展开更多
基金National Natural Science Foundation of China （60572023）
文摘In this paper, the marginal Rao-Blackwellized particle filter （MRBPF）, which fuses the Rao-Blackwellized particle filter （RBPF） algorithm and the marginal particle filter （MPF） algorithm, is presented. The state space is divided into linear and non-linear parts, which can be estimated separately by the MPF and the optional Kalman filter. Through simulation in the terrain aided navigation （TAN） domain, it is demonstrated that, compared with the RBPF, the root mean square errors （RMSE） and the error variance of the nonlinear state estimations by the proposed MRBPF are respectively reduced by 29% and 96%, while the unique particle count is increased by 80%. It is also found that the MRBPF has better convergence properties, and analysis has shown that the existing RBPF is nothing more than a special case of the MRBPF.
文摘This paper presents a modified Rao-Blackwellized Particle Filter (RBPF) approach for the bearing-only monocular SLAM problem. While FastSLAM 2.0 is known to be one of the most computationally efficient SLAM approaches;it is not applicable to certain formulations of the SLAM problem in which some of the states are not explicitly expressed in the measurement equation. This constraint impacts the versatility of the FastSLAM 2.0 in dealing with partially ob-servable systems, especially in dynamic environments where inclusion of higher order but unobservable states such as velocity and acceleration in the filtering process is highly desirable. In this paper, the formulation of an enhanced RBPF-based SLAM with proper sampling and importance weights calculation for resampling distributions is presented. As an example, the new formulation uses the higher order states of the pose of a monocular camera to carry out SLAM for a mobile robot. The results of the experiments on the robot verify the improved performance of the higher order RBPF under low parallax angles conditions.
基金supported by the Scientific and Technological Research Project Funds of Chongqing Municipal Education Commission (KJ130512)the Project Funds of Chongqing Science and Technology Commission (cstc2015jcyj B0241)
文摘For the problems of estimation accuracy, inconsistencies and robustness in mobile robot simultaneous localization and mapping （SLAM）, a novel SLAM based on improved Rao-Blackwellized H∞ particle filter （IRBHF-SLAM） algorithm is proposed. The iterated unscented H∞ filter （IUHF） is utilized to accurately calculate the importance density function, repeatedly correcting the state mean and the covariance matrix by the iterative update method. The laser sensor＇s observation information is introduced into sequential importance sampling routine. It can avoid the calculation of Jacobian matrix and linearization error accumulation; meanwhile, the robustness of the algorithm is enhanced. IRBHF-SLAM is compared with FastSLAM2.0 and the unscented FastSLAM （UFastSLAM） under different noises in simulation experiments. Results show the algorithm can improve the estimation accuracy and stability. The improved approach, based on the robot operation system （ROS）, runs on the Pioneer3-DX robot equipped with a HOKUYO URG-04LX （URG） laser range finder. Experimental results show the improved algorithm can reduce the required number of particles and the operating time; and create online 2 dimensional （2-D） grid-map with high precision in different environments.
基金supported by National Natural Science Foundation of China (Nos. 61075090, 61005092)
文摘A probabilistic algorithm is proposed for the problem of simultaneous robot localization and peopletracking （SLAP） using single onboard sensor in situations with sensor noise and global uncertainties over the observer＇s pose. By the decomposition of the joint distribution according to the Rao-Blackwell theorem, posteriors of the robot pose are sequentially estimated over time by a smoothed laser perception model and an improved resampling scheme with evolution strategies; the conditional distribution of the person＇s position is estimated using unscented Kalman filter （UKF） to deal with the nonlinear dynamic of human motion. Experiments conducted in a real indoor service robot scenario validate the favorable performance of the positional accuracy as well as the improved computational efficiency.
文摘The Rao-Blackwellized Particle Filter （RBPF） is widely used for high dimensional nonlinear sys- tems, often with a linear Gaussian substructure. However, the RBPF is just a specific method in the class of Rao-Blackwellized Filtering （RBF）. This paper analyzes the recursive structure of the RBF from a more gen- eral perspective. The research starts from a general system model and studies the interconnected relation- ships between the two subspaces during the iterations. The results illustrate the working mechanisms of the RBF with an extensible framework for easily building Rao-Blackwellized algorithms with common nonlinear filters. Several examples are given to illustrate how to build new filters using this framework.
文摘针对移动机器人同时定位与地图创建(Simultaneous localization and mapping,SLAM)中的FastSLAM算法,存在非线性系统线性化处理和计算雅可比矩阵的缺点,本文提出了基于Sterling多项式插值处理非线性系统的SLAM方法.该方法基于Rao-Blackwellized粒了滤波框架,利用中心差分滤波方法产生改进的建议分布函数,提高了机器人位姿估计的精度;利用中心差分滤波初始化特征和更新地图中的特征,提高了地图创建的精度;针对实际应用中存在虚假特征的情况提出了一种有效的地图管理方法.在同等粒了数的情况下,该方法改进了SLAM结果的精度.基于仿真和实际数据的实验结果验证了该方法的有效性.
