On the effectiveness of feature-based lidar point cloud registration

LIDAR systems have been regarded as novel technologies for efficiently acquiring 3-D geo-spatial information, resulting in broad applications in engineering and management fields. Registration of LIDAR point clouds of consecutive scans or different platforms is a prerequisite for fully exploiting advantages of afore-mentioned applications. In this study, the authors integrate point, line and plane features, commonly seen geometric primitives and readily detected or derived from point clouds, for establishing a multi-feature 3-D similarity transformation model, both functional and stochastic, and illustrate the feasibility of the proposed methodologies on the effectiveness of employed features through theoretical identifications and experimental demonstrations.