Simultaneous localization and mapping(SLAM)is one of the most attractive research hotspots in the field of robotics,and it is also a prerequisite for the autonomous navigation of robots.It can significantly improve th...Simultaneous localization and mapping(SLAM)is one of the most attractive research hotspots in the field of robotics,and it is also a prerequisite for the autonomous navigation of robots.It can significantly improve the autonomous navigation ability of mobile robots and their adaptability to different application environments and contribute to the realization of real-time obstacle avoidance and dynamic path planning.Moreover,the application of SLAM technology has expanded from industrial production,intelligent transportation,special operations and other fields to agricultural environments,such as autonomous navigation,independent weeding,three-dimen-sional(3D)mapping,and independent harvesting.This paper mainly introduces the principle,sys-tem framework,latest development and application of SLAM technology,especially in agricultural environments.Firstly,the system framework and theory of the SLAM algorithm are introduced,and the SLAM algorithm is described in detail according to different sensor types.Then,the devel-opment and application of SLAM in the agricultural environment are summarized from two aspects:environment map construction,and localization and navigation of agricultural robots.Finally,the challenges and future research directions of SLAM in the agricultural environment are discussed.展开更多
An innovative multi-robot simultaneous localization and mapping(SLAM)is proposed based on a mobile Ad hoc local wireless sensor network(Ad-WSN).Multiple followed-robots equipped with the wireless link RS232/485module ...An innovative multi-robot simultaneous localization and mapping(SLAM)is proposed based on a mobile Ad hoc local wireless sensor network(Ad-WSN).Multiple followed-robots equipped with the wireless link RS232/485module act as mobile nodes,with various on-board sensors,Tp-link wireless local area network cards,and Tp-link wireless routers.The master robot with embedded industrial PC and a complete robot control system autonomously performs the SLAM task by exchanging information with multiple followed-robots by using this self-organizing mobile wireless network.The PC on the remote console can monitor multi-robot SLAM on-site and provide direct motion control of the robots.This mobile Ad-WSN complements an environment devoid of usual GPS signals for the robots performing SLAM task in search and rescue environments.In post-disaster areas,the network is usually absent or variable and the site scene is cluttered with obstacles.To adapt to such harsh situations,the proposed self-organizing mobile Ad-WSN enables robots to complete the SLAM process while improving the performances of object of interest identification and exploration area coverage.The information of localization and mapping can communicate freely among multiple robots and remote PC control center via this mobile Ad-WSN.Therefore,the autonomous master robot runs SLAM algorithms while exchanging information with multiple followed-robots and with the remote PC control center via this local WSN environment.Simulations and experiments validate the improved performances of the exploration area coverage,object marked,and loop closure,which are adapted to search and rescue post-disaster cluttered environments.展开更多
To tackle the problem of simultaneous localization and mapping(SLAM) in dynamic environments, a novel algorithm using landscape theory of aggregation is presented. By exploiting the coherent explanation how actors for...To tackle the problem of simultaneous localization and mapping(SLAM) in dynamic environments, a novel algorithm using landscape theory of aggregation is presented. By exploiting the coherent explanation how actors form alignments in a game provided by the landscape theory of aggregation, the algorithm is able to explicitly deal with the ever-changing relationship between the static objects and the moving objects without any prior models of the moving objects. The effectiveness of the method has been validated by experiments in two representative dynamic environments: the campus road and the urban road.展开更多
Localization plays a vital role in the mobile robot navigation system and is a fundamental capability for autonomous movement.In an indoor environment,the current mainstream localization scheme uses two-dimensional(2D...Localization plays a vital role in the mobile robot navigation system and is a fundamental capability for autonomous movement.In an indoor environment,the current mainstream localization scheme uses two-dimensional(2D)laser light detection and ranging(LiDAR)to build an occupancy grid map with simultaneous localization and mapping(SLAM)technology;it then locates the robot based on the known grid map.However,such solutions work effectively only in those areas with salient geometrical features.For areas with repeated,symmetrical,or similar structures,such as a long corridor,the conventional particle filtering method will fail.To solve this crucial problem,this paper presents a novel coarse-to-fine paradigm that uses visual features to assist mobile robot localization in a long corridor.First,the mobile robot is remote-controlled to move from the starting position to the end along a middle line.In the moving process,a grid map is built using the laser-based SLAM method.At the same time,a visual map consisting of special images which are keyframes is created according to a keyframe selection strategy.The keyframes are associated with the robot’s poses through timestamps.Second,a moving strategy is proposed,based on the extracted range features of the laser scans,to decide on an initial rough position.This is vital for the mobile robot because it gives instructions on where the robot needs to move to adjust its pose.Third,the mobile robot captures images in a proper perspective according to the moving strategy and matches them with the image map to achieve a coarse localization.Finally,an improved particle filtering method is presented to achieve fine localization.Experimental results show that our method is effective and robust for global localization.The localization success rate reaches 98.8%while the average moving distance is only 0.31 m.In addition,the method works well when the mobile robot is kidnapped to another position in the corridor.展开更多
为了提高室内动态场景下定位与建图的准确性与实时性,提出了一种基于目标检测的室内动态场景同步定位与建图(simultaneous localization and mapping,SLAM)系统。利用目标检测的实时性,在传统ORB_SLAM2算法上结合YOLOv5目标检测网络识...为了提高室内动态场景下定位与建图的准确性与实时性,提出了一种基于目标检测的室内动态场景同步定位与建图(simultaneous localization and mapping,SLAM)系统。利用目标检测的实时性,在传统ORB_SLAM2算法上结合YOLOv5目标检测网络识别相机图像中的动态物体,生成动态识别框,根据动态特征点判别方法只将识别框内动态物体上的ORB特征点去除,利用剩余特征点进行相机位姿的估计,最后建立只含静态物体的稠密点云地图与八叉树地图。同时在机器人操作系统(robot operating system,ROS)下进行仿真,采用套接字(Socket)通信方式代替ROS中话题通信方式,将ORB_SLAM2算法与YOLOv5目标检测网络相结合,以提高定位与建图的实时性。在TUM数据集上进行多次实验结果表明,与ORB_SLAM2系统相比,本文系统相机位姿精确度大幅度提高,并且提高了每帧跟踪的处理速度。展开更多
基金supported by the National Key Research and Development Program(No.2022YFD2001704).
文摘Simultaneous localization and mapping(SLAM)is one of the most attractive research hotspots in the field of robotics,and it is also a prerequisite for the autonomous navigation of robots.It can significantly improve the autonomous navigation ability of mobile robots and their adaptability to different application environments and contribute to the realization of real-time obstacle avoidance and dynamic path planning.Moreover,the application of SLAM technology has expanded from industrial production,intelligent transportation,special operations and other fields to agricultural environments,such as autonomous navigation,independent weeding,three-dimen-sional(3D)mapping,and independent harvesting.This paper mainly introduces the principle,sys-tem framework,latest development and application of SLAM technology,especially in agricultural environments.Firstly,the system framework and theory of the SLAM algorithm are introduced,and the SLAM algorithm is described in detail according to different sensor types.Then,the devel-opment and application of SLAM in the agricultural environment are summarized from two aspects:environment map construction,and localization and navigation of agricultural robots.Finally,the challenges and future research directions of SLAM in the agricultural environment are discussed.
基金Projects(61573213,61473174,61473179)supported by the National Natural Science Foundation of ChinaProjects(ZR2015PF009,ZR2014FM007)supported by the Natural Science Foundation of Shandong Province,China+1 种基金Project(2014GGX103038)supported by the Shandong Province Science and Technology Development Program,ChinaProject(2014ZZCX04302)supported by the Special Technological Program of Transformation of Initiatively Innovative Achievements in Shandong Province,China
文摘An innovative multi-robot simultaneous localization and mapping(SLAM)is proposed based on a mobile Ad hoc local wireless sensor network(Ad-WSN).Multiple followed-robots equipped with the wireless link RS232/485module act as mobile nodes,with various on-board sensors,Tp-link wireless local area network cards,and Tp-link wireless routers.The master robot with embedded industrial PC and a complete robot control system autonomously performs the SLAM task by exchanging information with multiple followed-robots by using this self-organizing mobile wireless network.The PC on the remote console can monitor multi-robot SLAM on-site and provide direct motion control of the robots.This mobile Ad-WSN complements an environment devoid of usual GPS signals for the robots performing SLAM task in search and rescue environments.In post-disaster areas,the network is usually absent or variable and the site scene is cluttered with obstacles.To adapt to such harsh situations,the proposed self-organizing mobile Ad-WSN enables robots to complete the SLAM process while improving the performances of object of interest identification and exploration area coverage.The information of localization and mapping can communicate freely among multiple robots and remote PC control center via this mobile Ad-WSN.Therefore,the autonomous master robot runs SLAM algorithms while exchanging information with multiple followed-robots and with the remote PC control center via this local WSN environment.Simulations and experiments validate the improved performances of the exploration area coverage,object marked,and loop closure,which are adapted to search and rescue post-disaster cluttered environments.
基金Project(XK100070532)supported by Beijing Education Committee Cooperation Building Foundation,China
文摘To tackle the problem of simultaneous localization and mapping(SLAM) in dynamic environments, a novel algorithm using landscape theory of aggregation is presented. By exploiting the coherent explanation how actors form alignments in a game provided by the landscape theory of aggregation, the algorithm is able to explicitly deal with the ever-changing relationship between the static objects and the moving objects without any prior models of the moving objects. The effectiveness of the method has been validated by experiments in two representative dynamic environments: the campus road and the urban road.
基金supported by the National Natural Science Foundation of China(Nos.61703067,61803058,51604056,and 51775076)the Science and Technology Research Project of Chongqing Education Commission,China(No.KJ1704072)the Doctoral Talent Train Project of Chongqing University of Posts and Telecommunications,China(No.BYJS202006)。
文摘Localization plays a vital role in the mobile robot navigation system and is a fundamental capability for autonomous movement.In an indoor environment,the current mainstream localization scheme uses two-dimensional(2D)laser light detection and ranging(LiDAR)to build an occupancy grid map with simultaneous localization and mapping(SLAM)technology;it then locates the robot based on the known grid map.However,such solutions work effectively only in those areas with salient geometrical features.For areas with repeated,symmetrical,or similar structures,such as a long corridor,the conventional particle filtering method will fail.To solve this crucial problem,this paper presents a novel coarse-to-fine paradigm that uses visual features to assist mobile robot localization in a long corridor.First,the mobile robot is remote-controlled to move from the starting position to the end along a middle line.In the moving process,a grid map is built using the laser-based SLAM method.At the same time,a visual map consisting of special images which are keyframes is created according to a keyframe selection strategy.The keyframes are associated with the robot’s poses through timestamps.Second,a moving strategy is proposed,based on the extracted range features of the laser scans,to decide on an initial rough position.This is vital for the mobile robot because it gives instructions on where the robot needs to move to adjust its pose.Third,the mobile robot captures images in a proper perspective according to the moving strategy and matches them with the image map to achieve a coarse localization.Finally,an improved particle filtering method is presented to achieve fine localization.Experimental results show that our method is effective and robust for global localization.The localization success rate reaches 98.8%while the average moving distance is only 0.31 m.In addition,the method works well when the mobile robot is kidnapped to another position in the corridor.
