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.