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

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-green-blue-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).