A 3D pointcloud registration algorithm based on fast coherent point drift

Pointcloud registration has a number of applications in various research areas. Computational complexity and accuracy are two major concerns for a pointcloud registration algorithm. This paper proposes a novel Fast Coherent Point Drift (F-CPD) algorithm for 3D pointcloud registration. The original CPD method is very time-consuming. The situation becomes even worse when the number of points is large. In order to overcome the limitations of the original CPD algorithm, a global convergent squared iterative expectation maximization (gSQUAREM) scheme is proposed. The gSQUAREM scheme uses an iterative strategy to estimate the transformations and correspondences between two pointclouds. Experimental results on a synthetic dataset show that the proposed algorithm outperforms the original CPD algorithm and the Iterative Closest Point (ICP) algorithm in terms of both registration accuracy and convergence rate.