Global Optimisation for Point Cloud Registration with the Bees Algorithm

Feiying Lan, Marco Castellani, Yongjing Wang Wang, Senjing Zheng

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract

The problem of 3D registration entails the estimation of spatial transformation which best aligns two point sets. Iterative Closest Point is arguably the most popu-lar and one of the most effective algorithms for 3D registration at present. This al-gorithm uses singular value decomposition to obtain a least squares alignment of two point sets. As a greedy alignment procedure, Iterative Closest Point is liable to converge to sub-optimal solutions. In this study, the problem of 3D registration is addressed using the popular Bees Algorithm metaheuristics. Thanks to its global search approach, the Bees Algorithm is known to be highly impervious to sub-optimal convergence. To increase the efficiency of the search, singular value de-composition is used to exploit the search results of the Bees Algorithm. Experi-mental evidence showed that the proposed algorithm outperformed Iterative Closest Point in terms of consistency and precision and showed high robustness to noise in the point sets.
Original languageEnglish
Title of host publicationIntelligent Manufacturing and Production Optimisation – The Bees Algorithm Approach
EditorsDuc Truong Pham, Natalia Hartono
PublisherSpringer Nature
Publication statusPublished - 2022

Publication series

NameSpringer Series in Advanced Manufacturing
PublisherSpringer
ISSN (Print)2196-1735

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