Robust point clouds registration with point-to-point [formula omitted] distance constraints in large-scale metrology

Abstract

Registration of partially overlapping, featureless three-dimensional (3D) point sets with noise is a difficult problem in the applications of large-scale metrology (LSM). Existing approaches use the sparse iterative closest points (SICP) method applying the lp norm to decrease the influence of outliers during registration. However, we reveal in this study that sparse point-to-point becomes easily trapped into local minima in featureless point clouds registration, and the error landscape of sparse point-to-plane is too shallow to restrain the sliding due to the lack of constraints in the large flat areas. Also, point clouds sampled from the large flat areas cause the low-rank matrix of linear equation in estimating the transformation matrix. Hence, we propose using the point-to-point lp distance constraints to restrain the sliding along large flat areas. We further define a weighted enhanced lp distance (WELD) error metric to slacken the constraints and escape from the local minima. Moreover, WELD can improve stability with the full-rank linear equation in estimating the transformation matrix. To verify the capability of escaping from the local minima and restraining the sliding, we choose SICP and two other algorithms to be compared with our method in simulated and actual point clouds. The comparisons show that our method successfully can escape from the local minima and restrain the sliding to handle outliers and noisy featureless point clouds effectively. The source code is available at https://github.com/Timbersaw-wangzw/WES-ICP.