TY - JOUR
T1 - A method of partially overlapping point clouds registration based on differential evolution algorithm
AU - Zhang, Xuetao
AU - Yang, Ben
AU - Li, Yunhao
AU - Zuo, Changle
AU - Wang, Xuewei
AU - Zhang, Wanxu
N1 - Publisher Copyright:
© 2018 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2018/12
Y1 - 2018/12
N2 - 3D point cloud registration is a key technology in 3D point cloud processing, such as 3D reconstruction, object detection. Trimmed Iterative Closest Point algorithm is a prevalent method for registration of two partially overlapping clouds. However, it relies heavily on the initial value and is liable to be trapped in to local optimum. In this paper, we adapt the Differential Evolution algorithm to obtain global optimal solution. By design appropriate evolutionary operations, the algorithm can make the populations distributed more widely, and keep the individuals from concentrating to a local optimum. In the experiment, the proposed algorithm is compared with existing methods which are based on global optimization algorithm such as Genetic Algorithm and particle filters. And the results have demonstrated that the proposed algorithm is more robust and can converge to a good result in fewer generations.
AB - 3D point cloud registration is a key technology in 3D point cloud processing, such as 3D reconstruction, object detection. Trimmed Iterative Closest Point algorithm is a prevalent method for registration of two partially overlapping clouds. However, it relies heavily on the initial value and is liable to be trapped in to local optimum. In this paper, we adapt the Differential Evolution algorithm to obtain global optimal solution. By design appropriate evolutionary operations, the algorithm can make the populations distributed more widely, and keep the individuals from concentrating to a local optimum. In the experiment, the proposed algorithm is compared with existing methods which are based on global optimization algorithm such as Genetic Algorithm and particle filters. And the results have demonstrated that the proposed algorithm is more robust and can converge to a good result in fewer generations.
UR - https://www.scopus.com/pages/publications/85058910746
U2 - 10.1371/journal.pone.0209227
DO - 10.1371/journal.pone.0209227
M3 - 文章
C2 - 30576346
AN - SCOPUS:85058910746
SN - 1932-6203
VL - 13
JO - PLoS ONE
JF - PLoS ONE
IS - 12
M1 - e0209227
ER -