Plane-based Accurate Registration of Real-world Point Clouds

Abstract

Traditional 3D point clouds registration algorithms, based on Iterative Closest Point (ICP), rely on point matching of large point clouds. In well-structured environments, such as buildings, planes can be segmented and used for registration, similarly to the classical point-based ICP approach. Using planes tremendously reduces the number of inputs.In this article, an efficient plane-based registration algorithm is presented. The optimal transformation is estimated through a two-step approach, successively performing robust plane-toplane minimization and non-linear robust point-to-plane registration. Experiments on the Autonomous Systems Lab (ASL) benchmark dataset show that the proposed method enables to successfully register 100% of the scans from the three indoor sequences. Experiments also show that the proposed method is robust in large motion scenarios and more accurate than other state-of-the-art algorithms. Moreover, a new challenging dataset, LOOP'IN, is provided. It is composed of two loops in real-world indoor scenes, with a large number of scans captured with a 3D LiDAR. Tests led on this dataset show that the algorithm is able to register long sequences, to close loops and to build an incremental map of the explored environment.

Centralized RANSAC-Based Point Cloud Registration With Fast Convergence and High Accuracy

“…To tackle the two drawbacks: small convergence basin and the sensitivity to outliers and partial overlaps, the robust symmetric ICP [25] was proposed by using a symmetric point-to-plane distance metric. In well-structured environments, the plane-based approach [26] was proposed to segment planes for registration. To improve the Sparse ICP method [22], the point-to-point registration problem is first transformed to a majorization-minimization problem [27].…”