Iterative closest point algorithm based on improved RANSAC

Shouyang Song; Xiangjian Mai; Qingxiang Liu

doi:10.1117/12.3004744

16 August 2023 Iterative closest point algorithm based on improved RANSAC

Shouyang Song, Xiangjian Mai, Qingxiang Liu

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 1278711 (2023) https://doi.org/10.1117/12.3004744
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

Iterative Closest Point (ICP) algorithm is usually used for registration of three-dimensional model point clouds. It is a common and mature registration algorithm. The traditional ICP algorithm has certain mismatching and the point selection condition is relatively simple, we introduce an extra RANSAC mismatching removal step into the ICP algorithm. It takes extra consideration of the spatial geometry information of the point pair selection. It improves the accuracy of the algorithm while speeding up the convergence of registration. In addition, we discuss the influence of the number of points and the similarity threshold of the algorithm under the Stanford standard point cloud data set. Finally, Gaussian curvature is introduced to improve the accuracy of the algorithm and reduce the randomness and the time of the algorithm.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shouyang Song, Xiangjian Mai, and Qingxiang Liu "Iterative closest point algorithm based on improved RANSAC", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 1278711 (16 August 2023); https://doi.org/10.1117/12.3004744

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KEYWORDS

Point clouds

3D modeling

Reconstruction algorithms

Detection and tracking algorithms

Data modeling

Image registration

Computer science

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