For localisation of unknown non-cooperative targets in space,the existence of interference points causes inaccuracy of pose estimation while utilizing point cloud registration.To address this issue,this paper proposes...For localisation of unknown non-cooperative targets in space,the existence of interference points causes inaccuracy of pose estimation while utilizing point cloud registration.To address this issue,this paper proposes a new iterative closest point(ICP)algorithm combined with distributed weights to intensify the dependability and robustness of the non-cooperative target localisation.As interference points in space have not yet been extensively studied,we classify them into two broad categories,far interference points and near interference points.For the former,the statistical outlier elimination algorithm is employed.For the latter,the Gaussian distributed weights,simultaneously valuing with the variation of the Euclidean distance from each point to the centroid,are commingled to the traditional ICP algorithm.In each iteration,the weight matrix W in connection with the overall localisation is obtained,and the singular value decomposition is adopted to accomplish high-precision estimation of the target pose.Finally,the experiments are implemented by shooting the satellite model and setting the position of interference points.The outcomes suggest that the proposed algorithm can effectively suppress interference points and enhance the accuracy of non-cooperative target pose estimation.When the interference point number reaches about 700,the average error of angle is superior to 0.88°.展开更多
激光点云常规匹配算法是迭代最近点(Iterative Closest Point, ICP)算法,但其收敛速度慢、鲁棒性差,因此,提出一种融合多种优化算法的激光点云高效ICP配准方法。首先对点云体素滤波降采样,通过ISS算子提取关键点,采用快速点特征直方图(F...激光点云常规匹配算法是迭代最近点(Iterative Closest Point, ICP)算法,但其收敛速度慢、鲁棒性差,因此,提出一种融合多种优化算法的激光点云高效ICP配准方法。首先对点云体素滤波降采样,通过ISS算子提取关键点,采用快速点特征直方图(Fast Point Feature Histograms, FPFH)提取关键点特征,嵌入多核多线程并行处理模式(OpenMP)提高特征提取速度;然后基于提取的FPFH特征,使用采样一致性初始配准算法(Sample Consensus Initial Alignment, SAC-IA)进行相似特征点粗配准,获取点云集间的初始旋转平移变换矩阵;最后采用ICP算法进行精配准,同时采用最优节点优先(Best Bin First, BBF)优化K-D tree近邻搜索法来加速对应关系点对的搜索,并设定动态阈值消除错误对应点对,提高配准快速性和准确性。对两个实例的配准点云进行了实验验证,结果表明,提出的优化配准算法具有明显速度优势和精度优势。展开更多
提出了一种分层块状全局搜索到临近点局部搜索的改进迭代最近点(ICP)算法,用于进一步提高ICP算法的配准速度并消除点云缺失对点云配准的影响。该配准方法在粗略配准之后,以点云块为分层单元对模型点集进行选取,并对选取的少量模型点进...提出了一种分层块状全局搜索到临近点局部搜索的改进迭代最近点(ICP)算法,用于进一步提高ICP算法的配准速度并消除点云缺失对点云配准的影响。该配准方法在粗略配准之后,以点云块为分层单元对模型点集进行选取,并对选取的少量模型点进行全局搜索获取其对应最近点;然后,以这些模型点对应的最近点作为搜索中心,在场景点集中进行局部搜索,获取这些模型点的大量临近点的对应最近点;最后,剔除错误对应最近点对,并求取坐标变换。与基于KD-Tree的ICP算法和基于LS+HS(Logarithmic Search Combined with Hierarchical Model Point Selection)的ICP算法相比,该配准算法对Happy bunny扫描数据的配准速度分别提高了78%和24%;对Dragon扫描数据的配准速度分别提高了73%和30%。这些结果表明该算法可以快速、精确地实现三维点云间的配准。展开更多
基金supported by the National Natural Science Foundation of China(51875535)the Natural Science Foundation for Young Scientists of Shanxi Province(201901D211242201701D221017)。
文摘For localisation of unknown non-cooperative targets in space,the existence of interference points causes inaccuracy of pose estimation while utilizing point cloud registration.To address this issue,this paper proposes a new iterative closest point(ICP)algorithm combined with distributed weights to intensify the dependability and robustness of the non-cooperative target localisation.As interference points in space have not yet been extensively studied,we classify them into two broad categories,far interference points and near interference points.For the former,the statistical outlier elimination algorithm is employed.For the latter,the Gaussian distributed weights,simultaneously valuing with the variation of the Euclidean distance from each point to the centroid,are commingled to the traditional ICP algorithm.In each iteration,the weight matrix W in connection with the overall localisation is obtained,and the singular value decomposition is adopted to accomplish high-precision estimation of the target pose.Finally,the experiments are implemented by shooting the satellite model and setting the position of interference points.The outcomes suggest that the proposed algorithm can effectively suppress interference points and enhance the accuracy of non-cooperative target pose estimation.When the interference point number reaches about 700,the average error of angle is superior to 0.88°.
文摘提出了一种分层块状全局搜索到临近点局部搜索的改进迭代最近点(ICP)算法,用于进一步提高ICP算法的配准速度并消除点云缺失对点云配准的影响。该配准方法在粗略配准之后,以点云块为分层单元对模型点集进行选取,并对选取的少量模型点进行全局搜索获取其对应最近点;然后,以这些模型点对应的最近点作为搜索中心,在场景点集中进行局部搜索,获取这些模型点的大量临近点的对应最近点;最后,剔除错误对应最近点对,并求取坐标变换。与基于KD-Tree的ICP算法和基于LS+HS(Logarithmic Search Combined with Hierarchical Model Point Selection)的ICP算法相比,该配准算法对Happy bunny扫描数据的配准速度分别提高了78%和24%;对Dragon扫描数据的配准速度分别提高了73%和30%。这些结果表明该算法可以快速、精确地实现三维点云间的配准。
