Iterative-Reweighting-Based Robust Iterative-Closest-Point Method

Abstract

In point cloud registration applications, noise and poor initial conditions lead to many false matches. False matches significantly degrade registration accuracy and speed. A penalty function is adopted in many robust point-to-point registration methods to suppress the influence of false matches. However, after applying a penalty function, problems cannot be solved in their analytical forms based on the introduction of nonlinearity. Therefore, most existing methods adopt the descending method. In this paper, a novel iterative-reweighting-based method is proposed to overcome the limitations of existing methods. The proposed method iteratively solves the eigenvectors of a four-dimensional matrix, whereas the calculation of the descending method relies on solving an eight-dimensional matrix. Therefore, the proposed method can achieve increased computational efficiency. The proposed method was validated on simulated noise corruption data, and the results reveal that it obtains higher efficiency and precision than existing methods, particularly under very noisy conditions. Experimental results for the KITTI dataset demonstrate that the proposed method can be used in real-time localization processes with high accuracy and good efficiency.