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Accelerated Coherent Point Drift for Automatic Three-Dimensional Point Cloud Registration | IEEE Journals & Magazine | IEEE Xplore

Accelerated Coherent Point Drift for Automatic Three-Dimensional Point Cloud Registration


Abstract:

Fully automatic 3-D point cloud registration is a highly challenging task in light detection and ranging (LiDAR) remote sensing. The coherent point drift (CPD) algorithm ...Show More

Abstract:

Fully automatic 3-D point cloud registration is a highly challenging task in light detection and ranging (LiDAR) remote sensing. The coherent point drift (CPD) algorithm provides an appropriate solution for point cloud registration because of its high accuracy. However, real application of the traditional CPD algorithm is limited due to its demanding computational complexity. In this letter, we present a novel accelerated CPD (ACPD) algorithm for fast, accurate, and automatic registration of 3-D point clouds. First, a global squared iterative expectation–maximization (gSQUAREM) technique is integrated to the ACPD algorithm. Then, the dual-tree improved fast Gauss transform method is used to further accelerate the Gaussian summation process during the correspondence probability matrix calculation. Experimental results on two real data sets show that the proposed algorithm can perform fast and accurate registration on LiDAR point clouds.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 13, Issue: 2, February 2016)
Page(s): 162 - 166
Date of Publication: 22 December 2015

ISSN Information:


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