Title: Optimised ICP algorithm based on simulated-annealing strategy

Authors: Wei Huang; Hui Wang; Xinghong Ling

Addresses: Soochow College, Soochow University, China ' School of Computer Science and Technology, Soochow University, China ' School of Computer Science and Artificial Intelligence, Suzhou City University, China

Abstract: How to process the point cloud data is a research hotspot, among which point cloud registration directly affects synthesis results. The iterative closest point (ICP) algorithm is a common method. However, it requires initial distribution of the registration point cloud and usually falls into optimal solution trap. To address the problem, an optimised ICP algorithm based on a simulated annealing strategy is proposed, which divides the registration process into filtering, coarse registration and precise registration. In filtering process, denoising and down sampling are performed to reduce the data size and improve the subsequent iteration rate; then the point cloud with a closer initial distribution is obtained by coarse registration. Finally, in the precise registration, we introduce the simulated annealing strategy, avoiding the local optimum trap. Experiments show that our method has a higher accuracy rate and contributes to the generation of more accurate and complete models in 3D data reconstruction.

Keywords: iterative closest point; ICP; simulate annealing; point cloud registration; normal distributions transform; filtering.

DOI: 10.1504/IJCSE.2024.141349

International Journal of Computational Science and Engineering, 2024 Vol.27 No.5, pp.621 - 626

Received: 04 Mar 2023
Accepted: 06 Jun 2023
Published online: 09 Sep 2024 *

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