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
激光雷达在扫描周围环境时会产生部分杂乱且稀疏的点云,该类点云会在配准过程中产生过大的分布拟合误差和关联距离,进而影响配准算法的精度及同步定位与建图(simultaneous localization and mapping,SLAM)的效果。针对以上问题,提出了...激光雷达在扫描周围环境时会产生部分杂乱且稀疏的点云,该类点云会在配准过程中产生过大的分布拟合误差和关联距离,进而影响配准算法的精度及同步定位与建图(simultaneous localization and mapping,SLAM)的效果。针对以上问题,提出了一种基于分布优化配准的实时激光SLAM算法。设计了一个特征谱滤波器,该滤波器以归一化最小特征值为滤波对象,去除不符合设定分布的点云以减小分布拟合误差;提出了一个点云配准损失函数,对源点云和目标点云构成的联合协方差矩阵和误差项进行复合归一化,以减小关联距离过大的点在迭代求解过程中的干扰;设计了一个SLAM算法框架,该框架包含前端里程计、回环检测和后端优化等环节,兼容纯激光建图和激光/惯性融合建图,进而保证建图的精确性和一致性,并提高了算法的适应性。在公开数据集上进行了多组实验,实验结果表明,相较于现有SLAM算法,所提算法在精度和速度指标方面均具有较大优势。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°.
文摘激光雷达在扫描周围环境时会产生部分杂乱且稀疏的点云,该类点云会在配准过程中产生过大的分布拟合误差和关联距离,进而影响配准算法的精度及同步定位与建图(simultaneous localization and mapping,SLAM)的效果。针对以上问题,提出了一种基于分布优化配准的实时激光SLAM算法。设计了一个特征谱滤波器,该滤波器以归一化最小特征值为滤波对象,去除不符合设定分布的点云以减小分布拟合误差;提出了一个点云配准损失函数,对源点云和目标点云构成的联合协方差矩阵和误差项进行复合归一化,以减小关联距离过大的点在迭代求解过程中的干扰;设计了一个SLAM算法框架,该框架包含前端里程计、回环检测和后端优化等环节,兼容纯激光建图和激光/惯性融合建图,进而保证建图的精确性和一致性,并提高了算法的适应性。在公开数据集上进行了多组实验,实验结果表明,相较于现有SLAM算法,所提算法在精度和速度指标方面均具有较大优势。
