TY - GEN
T1 - Point Cloud Registration Using Evolutionary Algorithm
AU - Zhang, Gewei
AU - Gao, Zihong
AU - Huo, Junbo
AU - Ke, Liangjun
N1 - Publisher Copyright:
© 2021, Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - Point cloud registration is a hot research topic in computer vision and related areas. In this paper, inspired by Iterative Closest Point (ICP), two evolutionary algorithms, that is, Genetic Algorithm (GA) and Estimation of Distribution Algorithm (EDA), are designed to deal with the problem of registration. Our study shows that evolutionary algorithm is potential to be applied to this problem, it can obtain better results than ICP on the tested real-world datum. Moreover, EDA can provide better results than GA.
AB - Point cloud registration is a hot research topic in computer vision and related areas. In this paper, inspired by Iterative Closest Point (ICP), two evolutionary algorithms, that is, Genetic Algorithm (GA) and Estimation of Distribution Algorithm (EDA), are designed to deal with the problem of registration. Our study shows that evolutionary algorithm is potential to be applied to this problem, it can obtain better results than ICP on the tested real-world datum. Moreover, EDA can provide better results than GA.
KW - Estimation of distribution algorithm
KW - Evolutionary algorithm
KW - Genetic algorithm
KW - Point cloud registration
UR - https://www.scopus.com/pages/publications/85107517844
U2 - 10.1007/978-981-16-1354-8_7
DO - 10.1007/978-981-16-1354-8_7
M3 - 会议稿件
AN - SCOPUS:85107517844
SN - 9789811613531
T3 - Communications in Computer and Information Science
SP - 69
EP - 77
BT - Bio-Inspired Computing
A2 - Pan, Linqiang
A2 - Pang, Shangchen
A2 - Song, Tao
A2 - Gong, Faming
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020
Y2 - 23 October 2020 through 25 October 2020
ER -