Open AccessGround-based Multi-platform Point Clouds Registration
Author(s) -
Yuan Gao,
Mingchu Li
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/646/1/012034
Subject(s) - point cloud , computer vision , computer science , segmentation , artificial intelligence , point (geometry) , laser scanning , object (grammar) , image registration , lidar , computer graphics (images) , remote sensing , geography , laser , image (mathematics) , mathematics , geometry , optics , physics
Laser scanning system provides an efficient solution to rapidly acquire 3D information of large-scale scenes. Point clouds collected by laser scanning systems contain numerous objects with significant disparities in size, complicated and incomplete structures, holes, varied point densities, and huge data volumes, raising great challenges for automated point clouds registration, segmentation, and object detection. The dissertation presents a hierarchical merging based multi-platform point clouds registration algorithm to align MLS point clouds and unordered TLS point clouds from various scenes and validates its performance on nine challenging datasets. The algorithm improves the efficiency and accuracy of point cloud registration and enhances the registration ability of the algorithm for low-overlap and high-symmetry point clouds.