Mirrored Iterative Closest Point Algorithm for Missing Point Cloud Registration

Focusing on point cloud registration tasks, an effective missing point cloud registration algorithm, the mirrored iterative closest point was proposed, to improve the registration between point cloud pairs with low overlap. The core of the proposed algorithm is to establish the mirror-type corresponding correlation between the source point cloud and the target point cloud. The specific process is as follows: firstly, establish a forward correspondence between the source point cloud and the target point cloud to capture the characteristic points located in the overlapping regions. Then, establish a backward correspondence of overlapping regions to obtain a collection of reliable matching pairs. In the end, estimate the optimal rigid transformation matrix based on the reliable matching pairs. In addition, the KD tree construction and feature extension are optimized to improve the algorithm efficiency. The proposed algorithm relies only on matching pair sets in overlapping regions and has excellent robustness and anti-interference. The results of experiment on Stanford dataset show that, for datasets with low overlap, the algorithm is better than the previous algorithms in accuracy and efficiency, while for datasets with high overlap, the algorithm improves the accuracy by 28. 8% and the efficiency by 47. 9% on average. Experiments demonstrate that the algorithm registers missing point clouds quickly and reliably. © 2023 Xi'an Jiaotong University. All rights reserved.