The ICP algorithm based on stochastic approach

Abstract

3D reconstruction has been widely applied in medical images, industrial inspection, self-driving cars, and indoor modeling. The 3D model is built by the steps of data collection, point cloud registration, surface reconstruction, and texture mapping. In the process of data collection, due to the limited visibility of the scanning system, the scanner needs to scan multiple angles and then splice the data to obtain a complete point cloud model. The point clouds from different angles must be merged into a unified coordinate system, which is known as point cloud registration. The result of point cloud registration can directly affect the accuracy of the point cloud model; thus, point cloud registration is a key step in the construction of the point cloud model. The ICP (Iterative Closest Points) algorithm is the most known technique of the point cloud registration. The variational ICP problem can be solved not only by deterministic but also by stochastic methods. One of them is Grey Wolf Optimizer (GWO) algorithm. Recently, GWO has been applied to rough point clouds alignment. In the proposed paper, we apply the GWO approach to the realization of the point-to-point ICP algorithms. Computer simulation results are presented to illustrate the performance of the proposed algorithm.