Simultaneous localization and mapping （SLAM） is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major d...Simultaneous localization and mapping （SLAM） is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major drawbacks： one is particle set degeneracy due to lack of observation information in proposal distribution design of the particle filter; the other is errors accumulation caused by linearization of the nonlinear robot motion model and the nonlinear environment observation model. For the purpose of overcoming the above problems, a new iterated sigma point FastSLAM （ISP-FastSLAM） algorithm is proposed. The main contribution of the algorithm lies in the utilization of iterated sigma point Kalman filter （ISPKF）, which minimizes statistical linearization error through Gaussian-Newton iteration, to design an optimal proposal distribution of the particle filter and to estimate the environment landmarks. On the basis of Rao-Blackwellized particle filter, the proposed ISP-FastSLAM algorithm is comprised by two main parts： in the first part, an iterated sigma point particle filter （ISPPF） to localize the robot is proposed, in which the proposal distribution is accurately estimated by the ISPKF; in the second part, a set of ISPKFs is used to estimate the environment landmarks. The simulation test of the proposed ISP-FastSLAM algorithm compared with FastSLAM2.0 algorithm and Unscented FastSLAM algorithm is carried out, and the performances of the three algorithms are compared. The simulation and comparing results show that the proposed ISP-FastSLAM outperforms other two algorithms both in accuracy and in robustness. The proposed algorithm provides reference for the optimization research of FastSLAM algorithm.展开更多
Geo-monitoring provides quantitative and reliable information to identify hazards and adopt appropriate measures timely.However,this task inherently exposes monitoring staff to hazardous environments,especially in und...Geo-monitoring provides quantitative and reliable information to identify hazards and adopt appropriate measures timely.However,this task inherently exposes monitoring staff to hazardous environments,especially in underground settings.Since 2000s,robots have been widely applied in various fields and many studies have focused on establishing autonomous mobile robotic systems as well as solving the issue of underground navigation and mapping.However,only a few studies have conducted quantitative evaluations of these methods,and almost none have provided a systematic and comprehensive assessment of the suitability of mapping robots for underground geo-monitoring.In this study,a methodology for objective and quantitative assessment of the applicability of SLAM methods in underground geo-monitoring is proposed.This involves the development of an underground test field and some specific metrics,which allow detailed local accuracy analysis of point measurements,line segments,and areas using artificial targets.With this proposed methodology,a series of repeated experimental measurements has been performed with an autonomous driving robot and the selected LiDAR-and visual-based SLAM methods.The resulting point cloud was compared with the reference data measured by a total station and a terrestrial laser scanner.The accuracy and precision of the selected SLAM methods as well as the verifiability and reliability of the results are evaluated and discussed by analysing quantities such as the deviations of the control points coordinates,cloudto-cloud distances between the test and reference point cloud,normal vector,centre point coordinates and area of the planar objects.The results demonstrate that the HDL Graph SLAM achieves satisfactory precision,accuracy,and repeatability with a mean cloud-to-cloud distance of 0.12 m(with a standard deviation of 0.13 m)in an 80 m closed-loop measurement area.Although RTAB-Map exhibits better plane-capturing capabilities,the measurement results reveal instability and inaccuracies.展开更多
The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward...The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm.From the perspective of causality,key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is pro-posed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling meth-od for a variety of different typical fluctuation processes is proposed,and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example anal-ysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32%higher than that of traditional methods.展开更多
针对动态场景导致视觉定位与建图(simultaneous localization and mapping,SLAM)算法位姿估计精度低和地图质量差等问题,提出一种结合深度学习的动态视觉SLAM算法。该算法在ORB-SLAM3前端引入轻量化且目标识别率高的YOLO11n目标检测网络...针对动态场景导致视觉定位与建图(simultaneous localization and mapping,SLAM)算法位姿估计精度低和地图质量差等问题,提出一种结合深度学习的动态视觉SLAM算法。该算法在ORB-SLAM3前端引入轻量化且目标识别率高的YOLO11n目标检测网络,检测潜在动态区域,并结合Lucas-Kanade(LK)光流法识别其中的动态特征点,从而在剔除动态特征点的同时保留静态特征点,提高特征点利用率和位姿估计精度。此外,新增语义地图构建线程,通过去除YOLO11n识别到的动态物体点云,并融合前端提取的语义信息,实现静态语义地图的构建。在TUM数据集上的实验结果表明,相较于ORB-SLAM3,该算法在高动态序列数据集中的定位精度提升了95.02%,验证了该算法在动态环境下的有效性,能显著提升视觉SLAM系统的定位精度和地图构建质量。展开更多
Register transfer level mapping (RTLM) algorithm for technology mapping at RT level is presented,which supports current design methodologies using high level design and design reuse.The mapping rules implement a sou...Register transfer level mapping (RTLM) algorithm for technology mapping at RT level is presented,which supports current design methodologies using high level design and design reuse.The mapping rules implement a source ALU using target ALU.The source ALUs and the target ALUs are all represented by the general ALUs and the mapping rules are applied in the algorithm.The mapping rules are described in a table fashion.The graph clustering algorithm is a branch and bound algorithm based on the graph formulation of the mapping algorithm.The mapping algorithm suits well mapping of regularly structured data path.Comparisons are made between the experimental results generated by 1 greedy algorithm and graphclustering algorithm,showing the feasibility of presented algorithm.展开更多
煤矿井下视觉同步定位与地图构建SLAM(Simultaneous Localization and Mapping)应用中,光照变化与低纹理场景严重影响特征点的提取和匹配结果,导致位姿估计失败,影响定位精度。提出一种基于改进定向快速旋转二值描述符ORB(Oriented Fast...煤矿井下视觉同步定位与地图构建SLAM(Simultaneous Localization and Mapping)应用中,光照变化与低纹理场景严重影响特征点的提取和匹配结果,导致位姿估计失败,影响定位精度。提出一种基于改进定向快速旋转二值描述符ORB(Oriented Fast and Rotated Brief)-SLAM3算法的煤矿井下移动机器人双目视觉定位算法SL-SLAM。针对光照变化场景,在前端使用光照稳定性的Super-Point特征点提取网络替换原始ORB特征点提取算法,并提出一种特征点网格限定法,有效剔除无效特征点区域,增加位姿估计稳定性。针对低纹理场景,在前端引入稳定的线段检测器LSD(Line Segment Detector)线特征提取算法,并提出一种点线联合算法,按照特征点网格对线特征进行分组,根据特征点的匹配结果进行线特征匹配,降低线特征匹配复杂度,节约位姿估计时间。构建了点特征和线特征的重投影误差模型,在线特征残差模型中添加角度约束,通过点特征和线特征的位姿增量雅可比矩阵建立点线特征重投影误差统一成本函数。局部建图线程使用ORB-SLAM3经典的局部优化方法调整点、线特征和关键帧位姿,并在后端线程中进行回环修正、子图融合和全局捆绑调整BA(Bundle Adjustment)。在EuRoC数据集上的试验结果表明,SL-SLAM的绝对位姿误差APE(Absolute Pose Error)指标优于其他对比算法,并取得了与真值最接近的轨迹预测结果:均方根误差相较于ORB-SLAM3降低了17.3%。在煤矿井下模拟场景中的试验结果表明,SL-SLAM能适应光照变化和低纹理场景,可以满足煤矿井下移动机器人的定位精度和稳定性要求。展开更多
传统的温室作业方式依赖于人工操作,工作效率低且难以保证作业的质量和稳定性。温室自主导航系统可以实现温室内自动化导航和作业,提高温室作物的生产效率和品质。因此,设计一种定位与地图构建(Simultaneous Localization And Mapping, ...传统的温室作业方式依赖于人工操作,工作效率低且难以保证作业的质量和稳定性。温室自主导航系统可以实现温室内自动化导航和作业,提高温室作物的生产效率和品质。因此,设计一种定位与地图构建(Simultaneous Localization And Mapping, SLAM)技术下的温室自主导航系统,可利用激光雷达等传感器实时构建温室内的地图,并利用SLAM算法实现自主定位与导航。为了提高系统的鲁棒性和性能,提出了一种基于改进粒子滤波算法的姿态估计方法。试验结果表明:该温室自主导航系统能够高效准确地实现温室内的自主导航任务,为农业生产提供了一种新的自动化解决方案。展开更多
FastSLAM is a popular framework which uses a Rao-Blackwellized particle filter to solve the simultaneous localization and mapping problem（SLAM）. However, in this framework there are two important potential limitatio...FastSLAM is a popular framework which uses a Rao-Blackwellized particle filter to solve the simultaneous localization and mapping problem（SLAM）. However, in this framework there are two important potential limitations, the particle depletion problem and the linear approximations of the nonlinear functions. To overcome these two drawbacks, this paper proposes a new FastSLAM algorithm based on revised genetic resampling and square root unscented particle filter（SR-UPF）. Double roulette wheels as the selection operator, and fast Metropolis-Hastings（MH） as the mutation operator and traditional crossover are combined to form a new resampling method. Amending the particle degeneracy and keeping the particle diversity are both taken into considerations in this method. As SR-UPF propagates the sigma points through the true nonlinearity, it decreases the linearization errors. By directly transferring the square root of the state covariance matrix, SR-UPF has better numerical stability. Both simulation and experimental results demonstrate that the proposed algorithm can improve the diversity of particles, and perform well on estimation accuracy and consistency.展开更多
The purpose of this article is to propose a new hybrid projection method for a quasi-nonexpansive mapping. The strong convergence of the algorithm is proved in real Hilbert spaces. A numerical experiment is also inclu...The purpose of this article is to propose a new hybrid projection method for a quasi-nonexpansive mapping. The strong convergence of the algorithm is proved in real Hilbert spaces. A numerical experiment is also included to explain the effectiveness of the proposed methods. The results of this paper are interesting extensions of those known results.展开更多
In recent years, reconstructing a sparse map from a simultaneous localization and mapping(SLAM) system on a conventional CPU has undergone remarkable progress. However,obtaining a dense map from the system often requi...In recent years, reconstructing a sparse map from a simultaneous localization and mapping(SLAM) system on a conventional CPU has undergone remarkable progress. However,obtaining a dense map from the system often requires a highperformance GPU to accelerate computation. This paper proposes a dense mapping approach which can remove outliers and obtain a clean 3D model using a CPU in real-time. The dense mapping approach processes keyframes and establishes data association by using multi-threading technology. The outliers are removed by changing detections of associated vertices between keyframes. The implicit surface data of inliers is represented by a truncated signed distance function and fused with an adaptive weight. A global hash table and a local hash table are used to store and retrieve surface data for data-reuse. Experiment results show that the proposed approach can precisely remove the outliers in scene and obtain a dense 3D map with a better visual effect in real-time.展开更多
Simultaneous localization and mapping(SLAM)has been applied across a wide range of areas from robotics to automatic pilot.Most of the SLAM algorithms are based on the assumption that the noise is timeinvariant Gaussia...Simultaneous localization and mapping(SLAM)has been applied across a wide range of areas from robotics to automatic pilot.Most of the SLAM algorithms are based on the assumption that the noise is timeinvariant Gaussian distribution.In some cases,this assumption no longer holds and the performance of the traditional SLAM algorithms declines.In this paper,we present a robust SLAM algorithm based on variational Bayes method by modelling the observation noise as inverse-Wishart distribution with "harmonic mean".Besides,cubature integration is utilized to solve the problem of nonlinear system.The proposed algorithm can effectively solve the problem of filtering divergence for traditional filtering algorithm when suffering the time-variant observation noise,especially for heavy-tai led noise.To validate the algorithm,we compare it with other t raditional filtering algorithms.The results show the effectiveness of the algorithm.展开更多
基金supported by Open Foundation of State Key Laboratory of Robotics and System, China (Grant No. SKLRS-2009-ZD-04)National Natural Science Foundation of China (Grant No. 60909055, Grant No.61005070)Fundamental Research Funds for the Central Universities of China (Grant No. 2009JBZ001-2)
文摘Simultaneous localization and mapping （SLAM） is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major drawbacks： one is particle set degeneracy due to lack of observation information in proposal distribution design of the particle filter; the other is errors accumulation caused by linearization of the nonlinear robot motion model and the nonlinear environment observation model. For the purpose of overcoming the above problems, a new iterated sigma point FastSLAM （ISP-FastSLAM） algorithm is proposed. The main contribution of the algorithm lies in the utilization of iterated sigma point Kalman filter （ISPKF）, which minimizes statistical linearization error through Gaussian-Newton iteration, to design an optimal proposal distribution of the particle filter and to estimate the environment landmarks. On the basis of Rao-Blackwellized particle filter, the proposed ISP-FastSLAM algorithm is comprised by two main parts： in the first part, an iterated sigma point particle filter （ISPPF） to localize the robot is proposed, in which the proposal distribution is accurately estimated by the ISPKF; in the second part, a set of ISPKFs is used to estimate the environment landmarks. The simulation test of the proposed ISP-FastSLAM algorithm compared with FastSLAM2.0 algorithm and Unscented FastSLAM algorithm is carried out, and the performances of the three algorithms are compared. The simulation and comparing results show that the proposed ISP-FastSLAM outperforms other two algorithms both in accuracy and in robustness. The proposed algorithm provides reference for the optimization research of FastSLAM algorithm.
基金supported by the German Academic Scholarship Foundation,the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation,Project number 422117092)the Saxon Ministry of Science and Arts.
文摘Geo-monitoring provides quantitative and reliable information to identify hazards and adopt appropriate measures timely.However,this task inherently exposes monitoring staff to hazardous environments,especially in underground settings.Since 2000s,robots have been widely applied in various fields and many studies have focused on establishing autonomous mobile robotic systems as well as solving the issue of underground navigation and mapping.However,only a few studies have conducted quantitative evaluations of these methods,and almost none have provided a systematic and comprehensive assessment of the suitability of mapping robots for underground geo-monitoring.In this study,a methodology for objective and quantitative assessment of the applicability of SLAM methods in underground geo-monitoring is proposed.This involves the development of an underground test field and some specific metrics,which allow detailed local accuracy analysis of point measurements,line segments,and areas using artificial targets.With this proposed methodology,a series of repeated experimental measurements has been performed with an autonomous driving robot and the selected LiDAR-and visual-based SLAM methods.The resulting point cloud was compared with the reference data measured by a total station and a terrestrial laser scanner.The accuracy and precision of the selected SLAM methods as well as the verifiability and reliability of the results are evaluated and discussed by analysing quantities such as the deviations of the control points coordinates,cloudto-cloud distances between the test and reference point cloud,normal vector,centre point coordinates and area of the planar objects.The results demonstrate that the HDL Graph SLAM achieves satisfactory precision,accuracy,and repeatability with a mean cloud-to-cloud distance of 0.12 m(with a standard deviation of 0.13 m)in an 80 m closed-loop measurement area.Although RTAB-Map exhibits better plane-capturing capabilities,the measurement results reveal instability and inaccuracies.
基金funded by the State Grid Science and Technology Project“Research on Key Technologies for Prediction and Early Warning of Large-Scale Offshore Wind Power Ramp Events Based on Meteorological Data Enhancement”(4000-202318098A-1-1-ZN).
文摘The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm.From the perspective of causality,key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is pro-posed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling meth-od for a variety of different typical fluctuation processes is proposed,and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example anal-ysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32%higher than that of traditional methods.
文摘针对动态场景导致视觉定位与建图(simultaneous localization and mapping,SLAM)算法位姿估计精度低和地图质量差等问题,提出一种结合深度学习的动态视觉SLAM算法。该算法在ORB-SLAM3前端引入轻量化且目标识别率高的YOLO11n目标检测网络,检测潜在动态区域,并结合Lucas-Kanade(LK)光流法识别其中的动态特征点,从而在剔除动态特征点的同时保留静态特征点,提高特征点利用率和位姿估计精度。此外,新增语义地图构建线程,通过去除YOLO11n识别到的动态物体点云,并融合前端提取的语义信息,实现静态语义地图的构建。在TUM数据集上的实验结果表明,相较于ORB-SLAM3,该算法在高动态序列数据集中的定位精度提升了95.02%,验证了该算法在动态环境下的有效性,能显著提升视觉SLAM系统的定位精度和地图构建质量。
文摘Register transfer level mapping (RTLM) algorithm for technology mapping at RT level is presented,which supports current design methodologies using high level design and design reuse.The mapping rules implement a source ALU using target ALU.The source ALUs and the target ALUs are all represented by the general ALUs and the mapping rules are applied in the algorithm.The mapping rules are described in a table fashion.The graph clustering algorithm is a branch and bound algorithm based on the graph formulation of the mapping algorithm.The mapping algorithm suits well mapping of regularly structured data path.Comparisons are made between the experimental results generated by 1 greedy algorithm and graphclustering algorithm,showing the feasibility of presented algorithm.
文摘煤矿井下视觉同步定位与地图构建SLAM(Simultaneous Localization and Mapping)应用中,光照变化与低纹理场景严重影响特征点的提取和匹配结果,导致位姿估计失败,影响定位精度。提出一种基于改进定向快速旋转二值描述符ORB(Oriented Fast and Rotated Brief)-SLAM3算法的煤矿井下移动机器人双目视觉定位算法SL-SLAM。针对光照变化场景,在前端使用光照稳定性的Super-Point特征点提取网络替换原始ORB特征点提取算法,并提出一种特征点网格限定法,有效剔除无效特征点区域,增加位姿估计稳定性。针对低纹理场景,在前端引入稳定的线段检测器LSD(Line Segment Detector)线特征提取算法,并提出一种点线联合算法,按照特征点网格对线特征进行分组,根据特征点的匹配结果进行线特征匹配,降低线特征匹配复杂度,节约位姿估计时间。构建了点特征和线特征的重投影误差模型,在线特征残差模型中添加角度约束,通过点特征和线特征的位姿增量雅可比矩阵建立点线特征重投影误差统一成本函数。局部建图线程使用ORB-SLAM3经典的局部优化方法调整点、线特征和关键帧位姿,并在后端线程中进行回环修正、子图融合和全局捆绑调整BA(Bundle Adjustment)。在EuRoC数据集上的试验结果表明,SL-SLAM的绝对位姿误差APE(Absolute Pose Error)指标优于其他对比算法,并取得了与真值最接近的轨迹预测结果:均方根误差相较于ORB-SLAM3降低了17.3%。在煤矿井下模拟场景中的试验结果表明,SL-SLAM能适应光照变化和低纹理场景,可以满足煤矿井下移动机器人的定位精度和稳定性要求。
文摘传统的温室作业方式依赖于人工操作,工作效率低且难以保证作业的质量和稳定性。温室自主导航系统可以实现温室内自动化导航和作业,提高温室作物的生产效率和品质。因此,设计一种定位与地图构建(Simultaneous Localization And Mapping, SLAM)技术下的温室自主导航系统,可利用激光雷达等传感器实时构建温室内的地图,并利用SLAM算法实现自主定位与导航。为了提高系统的鲁棒性和性能,提出了一种基于改进粒子滤波算法的姿态估计方法。试验结果表明:该温室自主导航系统能够高效准确地实现温室内的自主导航任务,为农业生产提供了一种新的自动化解决方案。
基金supported by National Natural Science Foundation of China(No.61101197)Research Fund for the Doctoral Program of Higher Education of China(No.20093219120025)
文摘FastSLAM is a popular framework which uses a Rao-Blackwellized particle filter to solve the simultaneous localization and mapping problem（SLAM）. However, in this framework there are two important potential limitations, the particle depletion problem and the linear approximations of the nonlinear functions. To overcome these two drawbacks, this paper proposes a new FastSLAM algorithm based on revised genetic resampling and square root unscented particle filter（SR-UPF）. Double roulette wheels as the selection operator, and fast Metropolis-Hastings（MH） as the mutation operator and traditional crossover are combined to form a new resampling method. Amending the particle degeneracy and keeping the particle diversity are both taken into considerations in this method. As SR-UPF propagates the sigma points through the true nonlinearity, it decreases the linearization errors. By directly transferring the square root of the state covariance matrix, SR-UPF has better numerical stability. Both simulation and experimental results demonstrate that the proposed algorithm can improve the diversity of particles, and perform well on estimation accuracy and consistency.
基金The NSF(11071053)of ChinaNatural Science Basic Research Plan(2014JM2-1003)in Shaanxi Province of ChinaScientific Research Project(YD2016-12)of Yan’an University
文摘The purpose of this article is to propose a new hybrid projection method for a quasi-nonexpansive mapping. The strong convergence of the algorithm is proved in real Hilbert spaces. A numerical experiment is also included to explain the effectiveness of the proposed methods. The results of this paper are interesting extensions of those known results.
基金supported by the National Natural Science Foundation of China(61473202)。
文摘In recent years, reconstructing a sparse map from a simultaneous localization and mapping(SLAM) system on a conventional CPU has undergone remarkable progress. However,obtaining a dense map from the system often requires a highperformance GPU to accelerate computation. This paper proposes a dense mapping approach which can remove outliers and obtain a clean 3D model using a CPU in real-time. The dense mapping approach processes keyframes and establishes data association by using multi-threading technology. The outliers are removed by changing detections of associated vertices between keyframes. The implicit surface data of inliers is represented by a truncated signed distance function and fused with an adaptive weight. A global hash table and a local hash table are used to store and retrieve surface data for data-reuse. Experiment results show that the proposed approach can precisely remove the outliers in scene and obtain a dense 3D map with a better visual effect in real-time.
基金the National Natural Science Foundation of China(No.61803260)。
文摘Simultaneous localization and mapping(SLAM)has been applied across a wide range of areas from robotics to automatic pilot.Most of the SLAM algorithms are based on the assumption that the noise is timeinvariant Gaussian distribution.In some cases,this assumption no longer holds and the performance of the traditional SLAM algorithms declines.In this paper,we present a robust SLAM algorithm based on variational Bayes method by modelling the observation noise as inverse-Wishart distribution with "harmonic mean".Besides,cubature integration is utilized to solve the problem of nonlinear system.The proposed algorithm can effectively solve the problem of filtering divergence for traditional filtering algorithm when suffering the time-variant observation noise,especially for heavy-tai led noise.To validate the algorithm,we compare it with other t raditional filtering algorithms.The results show the effectiveness of the algorithm.
