空间位姿测量中面阵点云CPD方法的优化及验证

面阵激光成像雷达可以实现瞬态三维探测，适用于运动平台及非合作目标的位姿测量。针对相邻像元间具有串扰特性的面阵非均匀格网型稀疏点云，提出了一种适用于空间非合作目标位姿测量的多视角点云自动配准方法。该方法基于改进的相干点漂移(Coherent Point Drift, CPD)，将目标点云视为观测数据集，源点云视作为高斯混合模型(Gaussian mixture model, GMM)的质心点集。利用贝叶斯后验概率公式及期望最大化(Expectation-Maximum, EM)方法，对构造的GMM模型似然函数进行求解，在寻优过程中通过点云重叠特性对运算点集的权值参数进行自适应调整。对单次EM迭代后源点云间距离残差进行排序，选取最优变换点云对使用最近邻方法建立局部扰动量，得到每次漂移迭代的空间变换矩阵。为了避免陷入局部解，通过监督点云均方误差更新率，对参与漂移运算的点集属性进行交替。针对空间配准目标，建立了两种近似运行下的阵列成像仿真工况。试验结果表明：在强背景及像元模糊干扰下，该配准框架具有鲁棒性优势，其结果平均最大公共点集测度相对于粗+精组合配准框架提升约61%，可应用于空间面阵平台下...

Optimization and validation of coherent point drift for planar-array-based point cloud in space pose measurement

The planar-array-based imaging radar can achieve transient 3D detection and is suitable for pose measurement of moving platforms or non-cooperative targets. A multi-view point cloud auto-registration method for pose measurement of spatially non-cooperative targets was proposed for non-uniform grid point clouds with crosstalk characteristics between adjacent pixels. Based on the principle of improved coherent point drift (CPD), the method treats the target point cloud as the data distribution set and the source point cloud as the set of center-of-mass points of Gaussian mixture model (GMM). The likelihood function of the constructed GMM model is solved by using Bayesian posterior probability formula and Expectation-Maximum (EM), and the weight of the point set are adaptively adjusted by the overlap of the point clouds in the optimization process. The distance residuals between source point set after one EM iteration are ranked, the optimal transformed point cloud pair is selected, and the local perturbation quantity is established using the nearest neighbor method to obtain the spatial transformation matrix for each drift iteration. To avoid getting into local solutions, the attributes of the point set involved in the drift operation are alternated by supervising the mean square error update rate of the point cloud. For spatially targets, two simulation conditions are established to obtain multi-view non-cooperative target point cloud datasets. The results show that the method is robust under the strong noise and pixels blurring interference, and the average largest common point set corresponding is improved by approximately 61% compared with the other coarse-fine registration strategy, which can be applied to the non-cooperative target pose measurement under the spatial planar-array-based 3D imaging platform.