TY - GEN
T1 - Hybrid Global optimization Methods and Iterative Closest Point on Point-based Approach for 3D Registration
AU - Tao, Linh
AU - Nguyen, Tinh
AU - Nguyen, Trung
AU - Bui, Tam
N1 - Funding Information:
We would like to acknowledge the financial support from Natural Science Foundation of China (91845109, 21872169, 22109171, 22172190), Y.C. would like to acknowledge the support from the CAS Project for Young Scientists in Basic Research (YSBR-022) and the Young Cross Team Project of CAS (JCTD-2021-14). Z.C. would like to acknowledge the support from Jiangsu Planned Projects for Postdoctoral Researc Funds (2021K226B).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/10
Y1 - 2020/12/10
N2 - This paper proposes a novel approach to solve pair-wise registration problem which aligns different pointclouds taken from the same object or scenario at different angles. The new method uses points as a searching medium replacing convention six-dimensional one. By doing this, the number of searching dimensions is significantly reduced. Using the same number of searching loops, the new method results in a higher convergence rate into global optimal results. The approach is successfully implemented in a hybrid registration strategy which combines Iterative Closest Point (ICP) as a local aligning tool and a global searching algorithm such as state-of-the-arts including Simulated Annealing, Particle Swarm optimization, Differential Evolution or a recently developed adaptive Differential Evolution algorithm, ISADE. The accuracy and robustness of the new method over the conventional approach are proved through various experiments on different datasets.
AB - This paper proposes a novel approach to solve pair-wise registration problem which aligns different pointclouds taken from the same object or scenario at different angles. The new method uses points as a searching medium replacing convention six-dimensional one. By doing this, the number of searching dimensions is significantly reduced. Using the same number of searching loops, the new method results in a higher convergence rate into global optimal results. The approach is successfully implemented in a hybrid registration strategy which combines Iterative Closest Point (ICP) as a local aligning tool and a global searching algorithm such as state-of-the-arts including Simulated Annealing, Particle Swarm optimization, Differential Evolution or a recently developed adaptive Differential Evolution algorithm, ISADE. The accuracy and robustness of the new method over the conventional approach are proved through various experiments on different datasets.
KW - 3D Registration
KW - Global Searching
KW - Hybrid Registration
KW - ICP
KW - Point-based Registration
UR - http://www.scopus.com/inward/record.url?scp=85099775621&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099775621&partnerID=8YFLogxK
U2 - 10.1109/ICAMechS49982.2020.9310170
DO - 10.1109/ICAMechS49982.2020.9310170
M3 - Conference contribution
AN - SCOPUS:85099775621
T3 - International Conference on Advanced Mechatronic Systems, ICAMechS
SP - 192
EP - 197
BT - 2020 International Conference on Advanced Mechatronic Systems, ICAMechS 2020
PB - IEEE Computer Society
T2 - 2020 International Conference on Advanced Mechatronic Systems, ICAMechS 2020
Y2 - 10 December 2020 through 13 December 2020
ER -