Multi-perspective terrestrial LiDAR point cloud registration using planar primitives

摘要

Registering terrestrial LiDAR data points faces many challenges. Limited overlap between data sets, occlusions, and data density divergence greatly affect the registration performance. In this paper, we propose a method to identify the homologous points in different LiDAR acquisitions and join small but related primitives to maximize the correspondence between two data sets. Experiments were conducted with data sets collected using a Riegl VZ-400 terrestrial laser scanner. Our method greatly reduces the matching complexity from raw point measurements to planar primitives to clusters of spatially related structure features. The automatic registration method is able to achieve satisfactory results that put sets of LiDAR point clouds into correspondence with great accuracy.