Registration with the Point Cloud Library: A Modular Framework for Aligning in 3-D

Abstract

Registration is an important step when processing three-dimensional (3-D) point clouds. Applications for registration range from object modeling and tracking, to simultaneous localization and mapping (SLAM). This article presents the open-source point cloud library (PCL) and the tools available for point cloud registration. The PCL incorporates methods for the initial alignment of point clouds using a variety of local shape feature descriptors, as well as methods for refining initial alignments using different variants of the well-known iterative closest point (ICP) algorithm. This article provides an overview on registration algorithms, usage examples of their PCL implementations, and tips for their application. Since the choice and parameterization of the right algorithm for a particular type of data is one of the biggest problems in 3-D point cloud registration, we present three complete examples of data (and applications) and the respective registration pipeline in the PCL. These examples include dense red-greenblue-depth (RGB-D) point clouds acquired by consumer color and depth cameras, high-resolution laser scans from commercial 3-D scanners, and low-resolution sparse point clouds captured by a custom lightweight 3-D scanner on a microaerial vehicle (MAV). Registration of 3-D Point CloudsThe problem of consistently aligning two or more point clouds, i.e., sets of 3-D points, is inherent for 3-D registration. Often the point clouds are acquired by 3-D sensors from different viewpoints. The registration finds the relative pose (position and orientation) between views in a global coordinate frame, such that the overlapping areas between the point clouds match as well as possible; for two examples of registration see Figure 1. The overall objective of registration is to align individual point clouds and fuse them to a single point cloud so that subsequent

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“…10, two examples of testing point clouds are shown. In this test, the classical ICP method, the FPFH feature based alignment (FA) (Holz et al, 2015), and the Normal Distributions Transform (NDT) algorithm (Magnusson, 2009) are used as baseline for comparison. The initial angle difference between scans for the ICP and NDT methods is set to 0…”

Global Registration of 3D LiDAR Point Clouds Based on Scene Features: Application to Structured Environments

“…Holz et al created a pipeline including ICP to ensure the convergence of the algorithm [19]. First, a coarse alignment is determined, and then, a refinement step such as ICP or one of its variants is performed.…”

“…Various techniques were implemented to deal with such mismatches. Some of them aim at rejecting as many mismatches as possible using various criteria [19]. Zhou et al went further with the Fast Global Registration method (FGR) [26] introducing a new cost function.…”

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“…registration) [13]. W przypadku wykorzystania czujników RGB-D [16,31] problem rejestracji może być sformułowany jako znalezienie względ-nych transformacji między kolejnymi chmurami punktów oraz przekształce-nie współrzędnych wszystkich punktów do wspólnego układu odniesienia.…”