Simultaneous localization and mapping(SLAM)is widely used in many robot applications to acquire the unknown environment's map and the robots location.Graph-based SLAM is demonstrated to be effective in large-scale...Simultaneous localization and mapping(SLAM)is widely used in many robot applications to acquire the unknown environment's map and the robots location.Graph-based SLAM is demonstrated to be effective in large-scale scenarios,and it intuitively performs the SLAM as a pose graph.But because of the high data overlap rate,traditional graph-based SLAM is not efficient in some respects,such as real time performance and memory usage.To reduce1 data overlap rate,a graph-based SLAM with distributed submap strategy(DSS)is presented.In its front-end,submap based scan matching is processed and loop closing detection is conducted.Moreover in its back-end,pose graph is updated for global optimization and submap merging.From a series of experiments,it is demonstrated that graph-based SLAM with DSS reduces 51.79%data overlap rate,decreases 39.70%runtime and 24.60%memory usage.The advantages over other low overlap rate method is also proved in runtime,memory usage,accuracy and robustness performance.展开更多
When solving the problem of simultaneous localization and mapping(SLAM) ,a standard extended Kalman filter(EKF) is subject to linearization errors and causes optimistic estimation.This paper proposes a submap algorith...When solving the problem of simultaneous localization and mapping(SLAM) ,a standard extended Kalman filter(EKF) is subject to linearization errors and causes optimistic estimation.This paper proposes a submap algorithm,which builds a weighted least squares(WLS) constraint between two adjacent submaps according to the different estimations of the common features and the relationship between the vehicle poses in the corresponding submaps.By establishing the constraint equation after loop closing,re-linearization is implemented and each submap's reference frame tends to its equilibrium position quickly.Experimental results demonstrate that the algorithm could get a globally consistent map and linearization errors are limited in local regions.展开更多
针对机器人通讯范围受限的问题,提出一种新的多机器人协作同时定位与建图(simultaneous locali-zation and mapping,SLAM)方法。多个机器人采用基于最优控制的主动探索策略,创建自身周围区域的子地图,在每个建图周期内使用扩展的卡尔曼...针对机器人通讯范围受限的问题,提出一种新的多机器人协作同时定位与建图(simultaneous locali-zation and mapping,SLAM)方法。多个机器人采用基于最优控制的主动探索策略,创建自身周围区域的子地图,在每个建图周期内使用扩展的卡尔曼滤波器(extended Kalman filter,EKF)估计和维护子地图状态,并在一个周期结束后联络其通讯范围内的其他机器人,进行子地图的传递与融合。同时,为避免由通讯范围受限带来的地图过度融合问题,每个机器人保存每个建图周期内的局部子地图,待与其他机器人相遇时只传递并融合子地图的增量部分。仿真实验验证了该方法的有效性。展开更多
针对室内环境下的2D激光同步定位与制图(simultaneous localization and mapping,SLAM)问题,提出一种改进的扫描匹配方法,扫描到子图匹配。用连续的激光扫描帧构建子图,对齐新的扫描帧到邻近的子图以产生约束,通过高斯牛顿求解约束并估...针对室内环境下的2D激光同步定位与制图(simultaneous localization and mapping,SLAM)问题,提出一种改进的扫描匹配方法,扫描到子图匹配。用连续的激光扫描帧构建子图,对齐新的扫描帧到邻近的子图以产生约束,通过高斯牛顿求解约束并估计新的子图,利用Ceres优化来进行闭环,生成全局一致地图。经在室内条件下的测试,定位误差控制在0.4 m以下,制图误差控制在0.5 m左右,在激光匹配效率方面,相比传统方法提高了38.24%,实验结果表明,该方法可以有效提高定位与制图的精度和激光匹配效率。展开更多
This paper provides a brief review of the different optimisation strategies used in mobile robot simultaneous localisation and mapping(SLAM)problem.The focus is on the optimisation-based SLAM back end.The strategies a...This paper provides a brief review of the different optimisation strategies used in mobile robot simultaneous localisation and mapping(SLAM)problem.The focus is on the optimisation-based SLAM back end.The strategies are classified based on their purposes such as reducing the computational complexity,improving the convergence and improving the robustness.It is clearly pointed out that some approximations are made in some of the methods and there is always a trade-off between the computational complexity and the accuracy of the solution.The local submap joining is a strategy that has been used to address both the computational complexity and the convergence and is a flexible tool to be used in the SLAM back end.Although more research is needed to further improve the SLAM back end,nowadays there are quite a few relatively mature SLAM back end algorithms that can be used by SLAM researchers and users.展开更多
基金the Project Fund for Key Discipline of the Shanghai Municipal Education Commission(No.J50104)the Major State Basic Research Development Program of China(No.2017YFB0403500)。
文摘Simultaneous localization and mapping(SLAM)is widely used in many robot applications to acquire the unknown environment's map and the robots location.Graph-based SLAM is demonstrated to be effective in large-scale scenarios,and it intuitively performs the SLAM as a pose graph.But because of the high data overlap rate,traditional graph-based SLAM is not efficient in some respects,such as real time performance and memory usage.To reduce1 data overlap rate,a graph-based SLAM with distributed submap strategy(DSS)is presented.In its front-end,submap based scan matching is processed and loop closing detection is conducted.Moreover in its back-end,pose graph is updated for global optimization and submap merging.From a series of experiments,it is demonstrated that graph-based SLAM with DSS reduces 51.79%data overlap rate,decreases 39.70%runtime and 24.60%memory usage.The advantages over other low overlap rate method is also proved in runtime,memory usage,accuracy and robustness performance.
基金the Knowledge Innovation Program of Shanghai Science and Technology Committee (No.08510708300)the Ph.D.Programs Foundation of Ministry of Education of China (No.20070248097)
文摘When solving the problem of simultaneous localization and mapping(SLAM) ,a standard extended Kalman filter(EKF) is subject to linearization errors and causes optimistic estimation.This paper proposes a submap algorithm,which builds a weighted least squares(WLS) constraint between two adjacent submaps according to the different estimations of the common features and the relationship between the vehicle poses in the corresponding submaps.By establishing the constraint equation after loop closing,re-linearization is implemented and each submap's reference frame tends to its equilibrium position quickly.Experimental results demonstrate that the algorithm could get a globally consistent map and linearization errors are limited in local regions.
文摘This paper provides a brief review of the different optimisation strategies used in mobile robot simultaneous localisation and mapping(SLAM)problem.The focus is on the optimisation-based SLAM back end.The strategies are classified based on their purposes such as reducing the computational complexity,improving the convergence and improving the robustness.It is clearly pointed out that some approximations are made in some of the methods and there is always a trade-off between the computational complexity and the accuracy of the solution.The local submap joining is a strategy that has been used to address both the computational complexity and the convergence and is a flexible tool to be used in the SLAM back end.Although more research is needed to further improve the SLAM back end,nowadays there are quite a few relatively mature SLAM back end algorithms that can be used by SLAM researchers and users.
