Efficient Initial Guess Determination Based on 3D Point Cloud Projection for ICP Algorithms

Abstract

The standard Iterative Closest Point (ICP) algorithm is a robust and efficient rigid registration algorithm for 3D point clouds. Nevertheless, its efficiency notably decreases when applied to a large transformation cases. In order to avoid this drawback and improve its performance, we propose a new 3-step approach based on ICP and point Cloud Projection, called ICP- CP, that both enhances the accuracy in most cases and reduces the execution time. The first step consists in the determination of the best projection plane that preserves the topological structure of the point cloud. In the second, we compute an initial guess using the projected points. As to the third, it performs successive iterations until reaching the best transformation. The novelty of our approach is the use of the projected points that aims to find an adequate initialization for the ICP algorithm to ensure a fast convergence. We achieve a series of experimentations on both benchmarks and real clouds in order to validate our contribution and prove that, in most cases, the proposed algorithm is more accurate and robust than the standard ICP in a variety of situations, namely in case of large translation, large rotation and the both simultaneously.