A Study of Point Cloud Registration with Probability Product Kernel Functions

3D point cloud registration is an essential problem in 3D object and scene understanding. In many realistic circumstances, however, because of noise during data acquisition and large motion between two point clouds, most existing approaches can hardly work satisfactorily without good initial alignment or manually marked correspondences. Inspired by the popular kernel methods in machine learning community, this paper puts forward a general point cloud registration framework by constructing kernel functions over 3D point clouds. More specifically, Gaussian mixtures Based on the point clouds are established and probability product kernel functions are exploited for the registration. To enhance the generality of the framework, SE(3) on-manifold optimization scheme is employed to compute the optimal motion. Experimental results show that our registration framework works robustly when many outliers are presented and motion between point clouds is relatively large, and compares favorably to related methods.