K-means based RANSAC Algorithm for ICP Registration of 3D Point Cloud with Dense Outliers

In this work, a strategy for the 3D point cloud registration in the presence of multiple groups of outliers is addressed. Regarding to the point cloud registration, the iterative closed point (ICP) is a frequently used algorithm. Many related works have pointed out that robust point cloud matching can be achieved by using correspondence weight design or some other feature extraction techniques. However, it is interesting that whether it is possible to use traditional point-to-point ICP to deal with the point cloud registration in the presence of dense outlier clusters even without the aid of ICP weight design or point cloud feature extraction. To solve this question, a K-means based random sample consensus (RANSAC) strategy is presented. For a given data point clouds with high dense outliers, the K-means is firstly applied to cluster the point clouds. After that, the registration process cooperates with RANSAC's random cluster sampling for ICP matching, and calculates the sample with the highest matching score as the best candidate for point cloud matching. Here, we name this procedure as K-means based RANSAC ICP (KR-ICP). Through this point cloud registration strategy, the influence of multiple clusters of dense outliers on ICP registration can be avoided. Finally, this study verified the feasibility of this strategy via simulations. The proposed scheme can be extended to the related applications of point cloud initial pose alignment.