Adaptive weighted robust iterative closest point

Abstract

The Iterative Closest Point (ICP) algorithm is one of the most important methods for rigid registration between point sets. However, its performance begins to degenerate with the point data are overly contaminated by noise, outliers, and missing data. In this paper, we propose an adaptive weighted robust ICP method (AW-RICP). A sparse weight vector can be automatically learned by adaptive neighbors assigning process. The sparseness of the weight vector can be achieved by fitting the number of selected samples. The adaptive weight vector leads to the robustness of AW-RICP by eliminating the negative effect caused by point cloud pairs with largest registration errors. Further, the retained point pairs are assigned suitable weights to suppress the noises and outliers. The new error metric can effectively improve the robustness of ICP method. Additionally, we propose an efficient iterative solution to optimize our problem. Experiments on both synthetic and real data sets show that the proposed method can achieve superior performance with other state-of-the-art methods.