An improved coherent point drift method for tls point cloud registration of complex scenes

Processing unorganized 3D point clouds is highly desirable, especially for the applications in complex scenes (such as: mountainous or vegetation areas). Registration is the precondition to obtain complete surface information of complex scenes. However, for complex environment, the automatic registration of TLS point clouds is still a challenging problem. In this research, we propose an automatic registration for TLS point clouds of complex scenes based on coherent point drift (CPD) algorithm combined with a robust covariance descriptor. Out method consists of three steps: the construction of the covariance descriptor, uniform sampling of point clouds, and CPD optimization procedures based on Expectation-Maximization (EM algorithm). In the first step, we calculate a feature vector to construct a covariance matrix for each point based on the estimated normal vectors. In the subsequent step, to ensure efficiency, we use uniform sampling to obtain a small point set from the original TLS data. Finally, we form an objective function combining the geometric information described by the proposed descriptor, and optimize the transformation iteratively by maximizing the likelihood function. The experimental results on the TLS datasets of various scenes demonstrate the reliability and efficiency of the proposed method. Especially for complex environments with disordered vegetation or point density variations, this method can be much more efficient than original CPD algorithm