Precise iterative closest point algorithm for RGB-D data registration with noise and outliers

In this paper, we propose a precise Iterative Closest Point (ICP) algorithm which is a RGB information supplemented registration method based on correntropy measurement, which can overcome noise and outliers to complete precise point cloud registration. Firstly, in order to achieve accurate point set registration of single structure geometry point cloud with a large number of planes information rather than geometry information, we add the RGB information to our initial method as information compensation, which greatly ensure our algorithm could achieve accurate registration. Secondly, due to the noise and outliers existed in RGB-D data can easily cause deviations in registration, in order to achieve precise registration in RGB-D data, we introduced the correntropy measurement method into the initial model. Experimental results on our scene dataset demonstrate the proposed algorithm can achieve precise point set registration in RGB-D scene data well. (c) 2020 Elsevier B.V. All rights reserved.