移动机器人同步定位与建图问题(Simultaneous localization and mapping,SLAM)是机器人能否在未知环境中实现完全自主的关键问题之一.其中,机器人定位估计对于保持地图的一致性非常重要.本文分析了SLAM问题中机器人定位误差的收敛特性....移动机器人同步定位与建图问题(Simultaneous localization and mapping,SLAM)是机器人能否在未知环境中实现完全自主的关键问题之一.其中,机器人定位估计对于保持地图的一致性非常重要.本文分析了SLAM问题中机器人定位误差的收敛特性.分析表明随着机器人的运动,机器人定位误差总体上逐渐增大;在完全未知环境中无法预测机器人定位误差的上限.根据理论分析,本文提出了一种控制机器人定位误差在单位距离上增长速度的算法.该算法通过搜索获得满足定位误差限制的最佳的机器人运动速度,从而控制机器人定位误差的增长.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
A novel mobile robot simultaneous localization and mapping (SLAM) method is implemented by using the Rao- Blackwellized particle filter (RBPF) for monocular vision-based autonomous robot in unknown indoor environment....A novel mobile robot simultaneous localization and mapping (SLAM) method is implemented by using the Rao- Blackwellized particle filter (RBPF) for monocular vision-based autonomous robot in unknown indoor environment. The particle filter combined with unscented Kalman filter (UKF) for extending the path posterior by sampling new poses integrating the current observation. Landmark position estimation and update is implemented through UKF. Furthermore, the number of resampling steps is determined adaptively, which greatly reduces the particle depletion problem. Monocular CCD camera mounted on the robot tracks the 3D natural point landmarks structured with matching image feature pairs extracted through Scale Invariant Feature Transform (SIFT). The matching for multi-dimension SIFT features which are highly distinctive due to a special descriptor is implemented with a KD-Tree. Experiments on the robot Pioneer3 showed that our method is very precise and stable.展开更多
文摘移动机器人同步定位与建图问题(Simultaneous localization and mapping,SLAM)是机器人能否在未知环境中实现完全自主的关键问题之一.其中,机器人定位估计对于保持地图的一致性非常重要.本文分析了SLAM问题中机器人定位误差的收敛特性.分析表明随着机器人的运动,机器人定位误差总体上逐渐增大;在完全未知环境中无法预测机器人定位误差的上限.根据理论分析,本文提出了一种控制机器人定位误差在单位距离上增长速度的算法.该算法通过搜索获得满足定位误差限制的最佳的机器人运动速度,从而控制机器人定位误差的增长.
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.
基金Project (No. 2002AA735041) supported by the Hi-Tech Researchand Development Program (863) of China
文摘A novel mobile robot simultaneous localization and mapping (SLAM) method is implemented by using the Rao- Blackwellized particle filter (RBPF) for monocular vision-based autonomous robot in unknown indoor environment. The particle filter combined with unscented Kalman filter (UKF) for extending the path posterior by sampling new poses integrating the current observation. Landmark position estimation and update is implemented through UKF. Furthermore, the number of resampling steps is determined adaptively, which greatly reduces the particle depletion problem. Monocular CCD camera mounted on the robot tracks the 3D natural point landmarks structured with matching image feature pairs extracted through Scale Invariant Feature Transform (SIFT). The matching for multi-dimension SIFT features which are highly distinctive due to a special descriptor is implemented with a KD-Tree. Experiments on the robot Pioneer3 showed that our method is very precise and stable.
