Coarse–Fine Registration of Point Cloud Based on New Improved Whale Optimization Algorithm and Iterative Closest Point Algorithm

Abstract

In this paper, a point cloud coarse–fine registration method based on a new improved version of the whale optimization algorithm (NIWOA) and iterative closest point (ICP) algorithm is proposed; we use three strategies to improve the whale optimization algorithm (WOA). Firstly, circle chaotic mapping is used to initialize the whale population to increase the diversity of the population and make the search space more comprehensively explored. In addition, a Newton inertia weight is proposed to flexibly adjust the proportion of global exploration and local optimization in order to achieve the balance between the exploitation performance and exploration ability of the algorithm. At the same time, we introduce the nonlinear convergence factor that can adjust the size adaptively so that the algorithm can find the global optimal solution faster and more accurately, allowing it to avoid falling into the local optimal solution to a certain extent. The NIWOA algorithm is used to optimize the objective function of point cloud coarse registration to obtain the optimal coordinate transformation, and the rotation and translation operation is carried out on the registered point cloud. The obtained position is used as the initial pose of the ICP fine registration, and the final registration is achieved through ICP iteration. We conduct coarse registration experiments on multiple model point clouds and scene point clouds using the Stanford 3D Scanning Repository dataset and Princeton 3Dmatch dataset, respectively. The experimental results confirm that the NIWOA algorithm can not only find the initial position that is closer to the target point cloud, but also provide reliable initial values for the ICP algorithm. Meanwhile, the NIWOA algorithm combined with ICP experiment results show that the method has a higher registration accuracy and operation efficiency.