The point pair feature(PPF)is widely used for 6D pose estimation.In this paper,we propose an efficient 6D pose estimation method based on the PPF framework.We introduce a well-targeted down-sampling strategy that focu...The point pair feature(PPF)is widely used for 6D pose estimation.In this paper,we propose an efficient 6D pose estimation method based on the PPF framework.We introduce a well-targeted down-sampling strategy that focuses on edge areas for efficient feature extraction for complex geometry.A pose hypothesis validation approach is proposed to resolve ambiguity due to symmetry by calculating the edge matching degree.We perform evaluations on two challenging datasets and one real-world collected dataset,demonstrating the superiority of our method for pose estimation for geometrically complex,occluded,symmetrical objects.We further validate our method by applying it to simulated punctures.展开更多
Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface ...Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.展开更多
To fully describe the structure information of the point cloud when the LIDAR-object distance is long,a joint global and local feature(JGLF)descriptor is constructed.Compared with five typical descriptors,the object r...To fully describe the structure information of the point cloud when the LIDAR-object distance is long,a joint global and local feature(JGLF)descriptor is constructed.Compared with five typical descriptors,the object recognition rate of JGLF is higher when the LIDAR-object distances change.Under the situation that airborne LIDAR is getting close to the object,the particle filtering(PF)algorithm is used as the tracking frame.Particle weight is updated by comparing the difference between JGLFs to track the object.It is verified that the proposed algorithm performs 13.95%more accurately and stably than the basic PF algorithm.展开更多
Light detection and ranging(LiDAR)sensors play a vital role in acquiring 3D point cloud data and extracting valuable information about objects for tasks such as autonomous driving,robotics,and virtual reality(VR).Howe...Light detection and ranging(LiDAR)sensors play a vital role in acquiring 3D point cloud data and extracting valuable information about objects for tasks such as autonomous driving,robotics,and virtual reality(VR).However,the sparse and disordered nature of the 3D point cloud poses significant challenges to feature extraction.Overcoming limitations is critical for 3D point cloud processing.3D point cloud object detection is a very challenging and crucial task,in which point cloud processing and feature extraction methods play a crucial role and have a significant impact on subsequent object detection performance.In this overview of outstanding work in object detection from the 3D point cloud,we specifically focus on summarizing methods employed in 3D point cloud processing.We introduce the way point clouds are processed in classical 3D object detection algorithms,and their improvements to solve the problems existing in point cloud processing.Different voxelization methods and point cloud sampling strategies will influence the extracted features,thereby impacting the final detection performance.展开更多
供配电系统工况稳定是对空信息通信台站保持稳定高效运行的基础和前提条件。对空信息通信台站服务区域广阔、分布离散、数量庞大,亟需设计一种集中式的供配电数据采集系统对下属的对空信息通信台站供配电系统工况进行实时监控和管理。...供配电系统工况稳定是对空信息通信台站保持稳定高效运行的基础和前提条件。对空信息通信台站服务区域广阔、分布离散、数量庞大,亟需设计一种集中式的供配电数据采集系统对下属的对空信息通信台站供配电系统工况进行实时监控和管理。基于声卡模数转换单元获取供配电系统工况,综合应用云存储、Active Data Object接口、TCP/IP协议Socket程序、服务端多线程等技术,设计了一种数据采集系统,实现对各个站点供配电数据的采集、存储、查询和分析。相较于现有技术具有精度高、信息粒度小的优势,可支撑配电系统工况大数据挖掘分析应用。展开更多
Road boundary detection is essential for autonomous vehicle localization and decision-making,especially under GPS signal loss and lane discontinuities.For road boundary detection in structural environments,obstacle oc...Road boundary detection is essential for autonomous vehicle localization and decision-making,especially under GPS signal loss and lane discontinuities.For road boundary detection in structural environments,obstacle occlusions and large road curvature are two significant challenges.However,an effective and fast solution for these problems has remained elusive.To solve these problems,a speed and accuracy tradeoff method for LiDAR-based road boundary detection in structured environments is proposed.The proposed method consists of three main stages:1)a multi-feature based method is applied to extract feature points;2)a road-segmentation-line-based method is proposed for classifying left and right feature points;3)an iterative Gaussian Process Regression(GPR)is employed for filtering out false points and extracting boundary points.To demonstrate the effectiveness of the proposed method,KITTI datasets is used for comprehensive experiments,and the performance of our approach is tested under different road conditions.Comprehensive experiments show the roadsegmentation-line-based method can classify left,and right feature points on structured curved roads,and the proposed iterative Gaussian Process Regression can extract road boundary points on varied road shapes and traffic conditions.Meanwhile,the proposed road boundary detection method can achieve real-time performance with an average of 70.5 ms per frame.展开更多
LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box(BBox).However,under the three-dimensional space of autonomous driving scenes,the previou...LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box(BBox).However,under the three-dimensional space of autonomous driving scenes,the previous object detection methods,due to the pre-processing of the original LIDAR point cloud into voxels or pillars,lose the coordinate information of the original point cloud,slow detection speed,and gain inaccurate bounding box positioning.To address the issues above,this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++,which effectively preserves the original point cloud coordinate information.To improve the detection accuracy,a shell-based modeling method is proposed.It roughly determines which spherical shell the coordinates belong to.Then,the results are refined to ground truth,thereby narrowing the localization range and improving the detection accuracy.To improve the recall of 3D object detection with bounding boxes,this paper designs a self-attention module for 3D object detection with a skip connection structure.Some of these features are highlighted by weighting them on the feature dimensions.After training,it makes the feature weights that are favorable for object detection get larger.Thus,the extracted features are more adapted to the object detection task.Extensive comparison experiments and ablation experiments conducted on the KITTI dataset verify the effectiveness of our proposed method in improving recall and precision.展开更多
基金This work was supported in part by the National Key R&D Program of China(2018AAA0102200)National Natural Science Foundation of China(62132021,62102435,61902419,62002375,62002376)+2 种基金Natural Science Foundation of Hunan Province of China(2021JJ40696)Huxiang Youth Talent Support Program(2021RC3071)NUDT Research Grants(ZK19-30,ZK22-52).
文摘The point pair feature(PPF)is widely used for 6D pose estimation.In this paper,we propose an efficient 6D pose estimation method based on the PPF framework.We introduce a well-targeted down-sampling strategy that focuses on edge areas for efficient feature extraction for complex geometry.A pose hypothesis validation approach is proposed to resolve ambiguity due to symmetry by calculating the edge matching degree.We perform evaluations on two challenging datasets and one real-world collected dataset,demonstrating the superiority of our method for pose estimation for geometrically complex,occluded,symmetrical objects.We further validate our method by applying it to simulated punctures.
基金supported by the Future Challenge Program through the Agency for Defense Development funded by the Defense Acquisition Program Administration (No.UC200015RD)。
文摘Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.
基金This work was supported by the National Natural Science Foundation of China(Nos.61271353 and 61871389)Foundation of State Key Laboratory of Pulsed Power Laser Technology(No.SKL2018ZR09)Major Funding Projects of National University of Defense Technology(No.ZK18-01-02).
文摘To fully describe the structure information of the point cloud when the LIDAR-object distance is long,a joint global and local feature(JGLF)descriptor is constructed.Compared with five typical descriptors,the object recognition rate of JGLF is higher when the LIDAR-object distances change.Under the situation that airborne LIDAR is getting close to the object,the particle filtering(PF)algorithm is used as the tracking frame.Particle weight is updated by comparing the difference between JGLFs to track the object.It is verified that the proposed algorithm performs 13.95%more accurately and stably than the basic PF algorithm.
文摘Light detection and ranging(LiDAR)sensors play a vital role in acquiring 3D point cloud data and extracting valuable information about objects for tasks such as autonomous driving,robotics,and virtual reality(VR).However,the sparse and disordered nature of the 3D point cloud poses significant challenges to feature extraction.Overcoming limitations is critical for 3D point cloud processing.3D point cloud object detection is a very challenging and crucial task,in which point cloud processing and feature extraction methods play a crucial role and have a significant impact on subsequent object detection performance.In this overview of outstanding work in object detection from the 3D point cloud,we specifically focus on summarizing methods employed in 3D point cloud processing.We introduce the way point clouds are processed in classical 3D object detection algorithms,and their improvements to solve the problems existing in point cloud processing.Different voxelization methods and point cloud sampling strategies will influence the extracted features,thereby impacting the final detection performance.
文摘供配电系统工况稳定是对空信息通信台站保持稳定高效运行的基础和前提条件。对空信息通信台站服务区域广阔、分布离散、数量庞大,亟需设计一种集中式的供配电数据采集系统对下属的对空信息通信台站供配电系统工况进行实时监控和管理。基于声卡模数转换单元获取供配电系统工况,综合应用云存储、Active Data Object接口、TCP/IP协议Socket程序、服务端多线程等技术,设计了一种数据采集系统,实现对各个站点供配电数据的采集、存储、查询和分析。相较于现有技术具有精度高、信息粒度小的优势,可支撑配电系统工况大数据挖掘分析应用。
基金This work was supported by the Research on Construction and Simulation Technology of Hardware in Loop Testing Scenario for Self-Driving Electric Vehicle in China(2018YFB0105103J).
文摘Road boundary detection is essential for autonomous vehicle localization and decision-making,especially under GPS signal loss and lane discontinuities.For road boundary detection in structural environments,obstacle occlusions and large road curvature are two significant challenges.However,an effective and fast solution for these problems has remained elusive.To solve these problems,a speed and accuracy tradeoff method for LiDAR-based road boundary detection in structured environments is proposed.The proposed method consists of three main stages:1)a multi-feature based method is applied to extract feature points;2)a road-segmentation-line-based method is proposed for classifying left and right feature points;3)an iterative Gaussian Process Regression(GPR)is employed for filtering out false points and extracting boundary points.To demonstrate the effectiveness of the proposed method,KITTI datasets is used for comprehensive experiments,and the performance of our approach is tested under different road conditions.Comprehensive experiments show the roadsegmentation-line-based method can classify left,and right feature points on structured curved roads,and the proposed iterative Gaussian Process Regression can extract road boundary points on varied road shapes and traffic conditions.Meanwhile,the proposed road boundary detection method can achieve real-time performance with an average of 70.5 ms per frame.
基金This work was supported,in part,by the National Nature Science Foundation of China under grant numbers 62272236in part,by the Natural Science Foundation of Jiangsu Province under grant numbers BK20201136,BK20191401in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.
文摘LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box(BBox).However,under the three-dimensional space of autonomous driving scenes,the previous object detection methods,due to the pre-processing of the original LIDAR point cloud into voxels or pillars,lose the coordinate information of the original point cloud,slow detection speed,and gain inaccurate bounding box positioning.To address the issues above,this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++,which effectively preserves the original point cloud coordinate information.To improve the detection accuracy,a shell-based modeling method is proposed.It roughly determines which spherical shell the coordinates belong to.Then,the results are refined to ground truth,thereby narrowing the localization range and improving the detection accuracy.To improve the recall of 3D object detection with bounding boxes,this paper designs a self-attention module for 3D object detection with a skip connection structure.Some of these features are highlighted by weighting them on the feature dimensions.After training,it makes the feature weights that are favorable for object detection get larger.Thus,the extracted features are more adapted to the object detection task.Extensive comparison experiments and ablation experiments conducted on the KITTI dataset verify the effectiveness of our proposed method in improving recall and precision.
