Abstract
A three-dimensional (3D) point cloud registration based on entropy and particle swarm algorithm (EPSA) is proposed in the paper. The algorithm can effectively suppress noise and improve registration accuracy. Firstly, in order to find the k-nearest neighbor of point, the relationship of points is established by k-d tree. The noise is suppressed by the mean of neighbor points. Secondly, the gravity center of two point clouds is calculated to find the translation matrix T. Thirdly, the rotation matrix R is gotten through particle swarm optimization (PSO). While performing the PSO, the entropy information is selected as the fitness function. Lastly, the experiment results are presented. They demonstrate that the algorithm is valuable and robust. It can effectively improve the accuracy of rigid registration.
Highlights
Rigid registrationFor the original point cloud P and the target one Q, where there are a lot of overlapping between the two, the rigid registration is to find the rotation matrix R and the translation matrix T to transform the original point cloud to the target one
It is used as the fitness function in particle swarm optimization (PSO) to search the best R
We propose entropy and particle swarm algorithm (EPSA) to achieve rigid registration
Summary
For the original point cloud P and the target one Q, where there are a lot of overlapping between the two, the rigid registration is to find the rotation matrix R and the translation matrix T to transform the original point cloud to the target one. The nearest point method can be solved by establishing constraint potential consistency correspondence. Jost and Hugli[31] proposed a method that speeds up the iteration of ICP with the above coarse to fine search technology and is refined gradually to obtain a more reliable consistency correspondence. The method of calculating the entropy of the point cloud is introduced in this article. It is used as the fitness function in particle swarm optimization (PSO) to search the best R. Point cloud registration is used to seek consistent correspondence between different datasets and to transform the different coordinate systems into the same coordinate system to gather the full data of object. How to remove the noise is a challenge work which we have to face
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
