多机器人系统的通信状况能够直接影响协作同时定位与地图创建(Cooperative simultaneous localization and mapping,CSLAM)算法的设计和实现.根据对多机器人通信状况所作出假设的侧重点不同,对多机器人CSLAM算法研究现状和进展进行综述...多机器人系统的通信状况能够直接影响协作同时定位与地图创建(Cooperative simultaneous localization and mapping,CSLAM)算法的设计和实现.根据对多机器人通信状况所作出假设的侧重点不同,对多机器人CSLAM算法研究现状和进展进行综述.首先,简要介绍了基于完全连通通信条件的集中式CSLAM算法的特点和缺陷;其次,结合多机器人系统初始相对位姿关系未知的情况,从地图配准、数据关联和地图融合等三个方面,对基于通信范围或者带宽受限条件的分布式CSLAM算法的地图合并问题进行了分析和阐述;进而重点对考虑稀疏–动态通信状况的分布式CSLAM算法的最新研究成果进行了归纳总结.最后指出多机器人CSLAM研究领域今后的研究方向.展开更多
对于移动机器人研究领域来说,现阶段研究热点是如何在全球定位系统失效的情况下同时定位与地图构建(simultaneous localization and mapping,SLAM)。对于单个机器人SLAM已经有很多解决方案,然而当转移到多机器人平台时,对于存在的问题...对于移动机器人研究领域来说,现阶段研究热点是如何在全球定位系统失效的情况下同时定位与地图构建(simultaneous localization and mapping,SLAM)。对于单个机器人SLAM已经有很多解决方案,然而当转移到多机器人平台时,对于存在的问题又面临很多新的挑战。本文首先分析了多机器人SLAM,着重探讨了多机器人SLAM后端优化算法。分析了多机器人SLAM研究过程中遇到的不同问题,以及现阶段这些问题的处理算法。讨论了多机器人SLAM中扩展卡尔曼滤波、扩展信息滤波、粒子滤波、基于图优化的SLAM、地图融合等后端优化算法的研究现状,分析了算法的优缺点,并提出了未来发展的方向。展开更多
For a multi-robot system,the accurate global map building based on a local map obtained by a single robot is an essential issue.The map building process is always divided into three stages:single-robot map acquisition...For a multi-robot system,the accurate global map building based on a local map obtained by a single robot is an essential issue.The map building process is always divided into three stages:single-robot map acquisition,multi-robot map transmission,and multi-robot map merging.Based on the different stages of map building,this paper proposes a multi-stage optimization(MSO)method to improve the accuracy of the global map.In the map acquisition stage,we windowed the map based on the position of the robot to obtain the local map.Furthermore,we adopted the extended Kalman filter(EKF)to improve the positioning accuracy,thereby enhancing the accuracy of the map acquisition by the single robot.In the map transmission stage,considering the robustness of the multi-robot system in the real environment,we designed a dynamic self-organized communication topology(DSCT)based on the master and slave sketch to ensure the efficiency and accuracy of map transferring.In the map merging stage,multi-layer information filtering(MLIF)was investigated to increase the accuracy of the global map.We performed simulation experiments on the Gazebo platform and compared the result of the proposed method with that of classic map building methods.In addition,the practicability of this method has been verified on the Turtlebot3 burger robot.Experimental results proved that the MSO method improves the accuracy of the global map built by the multi-robot system.展开更多
针对单机器人同时定位与地图构建(Simultaneous Localization And Mapping,SLAM)算法构图效率低的问题,设计了基于ORB-SLAM3的多机器人协作SLAM的实时融合方案。首先,向两个SLAM进程输入相同的关键帧并以此帧所在位姿为世界坐标系完成...针对单机器人同时定位与地图构建(Simultaneous Localization And Mapping,SLAM)算法构图效率低的问题,设计了基于ORB-SLAM3的多机器人协作SLAM的实时融合方案。首先,向两个SLAM进程输入相同的关键帧并以此帧所在位姿为世界坐标系完成初始化,然后各机器人以此坐标系为基础完成定位于建图,同时将地图汇总融合完成任务。此方案避开了传统地图融合问题中特征匹配与位姿估计的技术难点。在Gazebo仿真环境进行测试实验,验证了方案的有效性、实时性和鲁棒性。展开更多
文摘多机器人系统的通信状况能够直接影响协作同时定位与地图创建(Cooperative simultaneous localization and mapping,CSLAM)算法的设计和实现.根据对多机器人通信状况所作出假设的侧重点不同,对多机器人CSLAM算法研究现状和进展进行综述.首先,简要介绍了基于完全连通通信条件的集中式CSLAM算法的特点和缺陷;其次,结合多机器人系统初始相对位姿关系未知的情况,从地图配准、数据关联和地图融合等三个方面,对基于通信范围或者带宽受限条件的分布式CSLAM算法的地图合并问题进行了分析和阐述;进而重点对考虑稀疏–动态通信状况的分布式CSLAM算法的最新研究成果进行了归纳总结.最后指出多机器人CSLAM研究领域今后的研究方向.
文摘对于移动机器人研究领域来说,现阶段研究热点是如何在全球定位系统失效的情况下同时定位与地图构建(simultaneous localization and mapping,SLAM)。对于单个机器人SLAM已经有很多解决方案,然而当转移到多机器人平台时,对于存在的问题又面临很多新的挑战。本文首先分析了多机器人SLAM,着重探讨了多机器人SLAM后端优化算法。分析了多机器人SLAM研究过程中遇到的不同问题,以及现阶段这些问题的处理算法。讨论了多机器人SLAM中扩展卡尔曼滤波、扩展信息滤波、粒子滤波、基于图优化的SLAM、地图融合等后端优化算法的研究现状,分析了算法的优缺点,并提出了未来发展的方向。
基金supported in part by the National Natural Science Foundation of China(Nos.61671041 and 61806119)the Shaanxi Key Laboratory of Integrated and Intelligent Navigation(No.SKLIIN-20190201).
文摘For a multi-robot system,the accurate global map building based on a local map obtained by a single robot is an essential issue.The map building process is always divided into three stages:single-robot map acquisition,multi-robot map transmission,and multi-robot map merging.Based on the different stages of map building,this paper proposes a multi-stage optimization(MSO)method to improve the accuracy of the global map.In the map acquisition stage,we windowed the map based on the position of the robot to obtain the local map.Furthermore,we adopted the extended Kalman filter(EKF)to improve the positioning accuracy,thereby enhancing the accuracy of the map acquisition by the single robot.In the map transmission stage,considering the robustness of the multi-robot system in the real environment,we designed a dynamic self-organized communication topology(DSCT)based on the master and slave sketch to ensure the efficiency and accuracy of map transferring.In the map merging stage,multi-layer information filtering(MLIF)was investigated to increase the accuracy of the global map.We performed simulation experiments on the Gazebo platform and compared the result of the proposed method with that of classic map building methods.In addition,the practicability of this method has been verified on the Turtlebot3 burger robot.Experimental results proved that the MSO method improves the accuracy of the global map built by the multi-robot system.
文摘针对单机器人同时定位与地图构建(Simultaneous Localization And Mapping,SLAM)算法构图效率低的问题,设计了基于ORB-SLAM3的多机器人协作SLAM的实时融合方案。首先,向两个SLAM进程输入相同的关键帧并以此帧所在位姿为世界坐标系完成初始化,然后各机器人以此坐标系为基础完成定位于建图,同时将地图汇总融合完成任务。此方案避开了传统地图融合问题中特征匹配与位姿估计的技术难点。在Gazebo仿真环境进行测试实验,验证了方案的有效性、实时性和鲁棒性。
