Pre-registration of arbitrarily oriented 3D surfaces using a genetic algorithm

Evgeny Lomonosov, D. Chetverikov, Anikó Ekárt

Research output: Contribution to journalArticle

69 Citations (Scopus)

Abstract

This paper reports on a successful application of genetic optimisation in 3D data registration. We consider the problem of Euclidean alignment of two arbitrarily oriented, partially overlapping surfaces represented by measured point sets contaminated by noise and outliers. Recently, we have proposed the Trimmed Iterative Closest Point algorithm (TrICP) [Chetverikov, D., Stepanov, D., Krsek, P., (2005). Robust Euclidean alignment of 3d point sets: the trimmed iterative closest point algorithm. Image Vision Comput. 23, 299-309] which is fast, applicable to overlaps under 50% and robust to erroneous and incomplete measurements. However, like other iterative methods, TrICP only works with roughly pre-registered surfaces. In this study, we propose a genetic algorithm for pre-alignment of arbitrarily oriented surfaces. Precision and robustness of TrICP are combined with generality of genetic algorithms. This results in a precise and fully automatic 3D data alignment system that needs no manual pre-registration.

Original languageEnglish
Pages (from-to)1201-1208
Number of pages8
JournalPattern Recognition Letters
Volume27
Issue number11
DOIs
Publication statusPublished - Aug 2006

Fingerprint

Genetic algorithms
Iterative methods

Keywords

  • 3D registration
  • Data alignment
  • Genetic algorithms
  • Point sets

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Pre-registration of arbitrarily oriented 3D surfaces using a genetic algorithm. / Lomonosov, Evgeny; Chetverikov, D.; Ekárt, Anikó.

In: Pattern Recognition Letters, Vol. 27, No. 11, 08.2006, p. 1201-1208.

Research output: Contribution to journalArticle

Lomonosov, Evgeny ; Chetverikov, D. ; Ekárt, Anikó. / Pre-registration of arbitrarily oriented 3D surfaces using a genetic algorithm. In: Pattern Recognition Letters. 2006 ; Vol. 27, No. 11. pp. 1201-1208.
@article{772116fee43c4d8bb37b5f16f1860ef5,
title = "Pre-registration of arbitrarily oriented 3D surfaces using a genetic algorithm",
abstract = "This paper reports on a successful application of genetic optimisation in 3D data registration. We consider the problem of Euclidean alignment of two arbitrarily oriented, partially overlapping surfaces represented by measured point sets contaminated by noise and outliers. Recently, we have proposed the Trimmed Iterative Closest Point algorithm (TrICP) [Chetverikov, D., Stepanov, D., Krsek, P., (2005). Robust Euclidean alignment of 3d point sets: the trimmed iterative closest point algorithm. Image Vision Comput. 23, 299-309] which is fast, applicable to overlaps under 50{\%} and robust to erroneous and incomplete measurements. However, like other iterative methods, TrICP only works with roughly pre-registered surfaces. In this study, we propose a genetic algorithm for pre-alignment of arbitrarily oriented surfaces. Precision and robustness of TrICP are combined with generality of genetic algorithms. This results in a precise and fully automatic 3D data alignment system that needs no manual pre-registration.",
keywords = "3D registration, Data alignment, Genetic algorithms, Point sets",
author = "Evgeny Lomonosov and D. Chetverikov and Anik{\'o} Ek{\'a}rt",
year = "2006",
month = "8",
doi = "10.1016/j.patrec.2005.07.018",
language = "English",
volume = "27",
pages = "1201--1208",
journal = "Pattern Recognition Letters",
issn = "0167-8655",
publisher = "Elsevier",
number = "11",

}

TY - JOUR

T1 - Pre-registration of arbitrarily oriented 3D surfaces using a genetic algorithm

AU - Lomonosov, Evgeny

AU - Chetverikov, D.

AU - Ekárt, Anikó

PY - 2006/8

Y1 - 2006/8

N2 - This paper reports on a successful application of genetic optimisation in 3D data registration. We consider the problem of Euclidean alignment of two arbitrarily oriented, partially overlapping surfaces represented by measured point sets contaminated by noise and outliers. Recently, we have proposed the Trimmed Iterative Closest Point algorithm (TrICP) [Chetverikov, D., Stepanov, D., Krsek, P., (2005). Robust Euclidean alignment of 3d point sets: the trimmed iterative closest point algorithm. Image Vision Comput. 23, 299-309] which is fast, applicable to overlaps under 50% and robust to erroneous and incomplete measurements. However, like other iterative methods, TrICP only works with roughly pre-registered surfaces. In this study, we propose a genetic algorithm for pre-alignment of arbitrarily oriented surfaces. Precision and robustness of TrICP are combined with generality of genetic algorithms. This results in a precise and fully automatic 3D data alignment system that needs no manual pre-registration.

AB - This paper reports on a successful application of genetic optimisation in 3D data registration. We consider the problem of Euclidean alignment of two arbitrarily oriented, partially overlapping surfaces represented by measured point sets contaminated by noise and outliers. Recently, we have proposed the Trimmed Iterative Closest Point algorithm (TrICP) [Chetverikov, D., Stepanov, D., Krsek, P., (2005). Robust Euclidean alignment of 3d point sets: the trimmed iterative closest point algorithm. Image Vision Comput. 23, 299-309] which is fast, applicable to overlaps under 50% and robust to erroneous and incomplete measurements. However, like other iterative methods, TrICP only works with roughly pre-registered surfaces. In this study, we propose a genetic algorithm for pre-alignment of arbitrarily oriented surfaces. Precision and robustness of TrICP are combined with generality of genetic algorithms. This results in a precise and fully automatic 3D data alignment system that needs no manual pre-registration.

KW - 3D registration

KW - Data alignment

KW - Genetic algorithms

KW - Point sets

UR - http://www.scopus.com/inward/record.url?scp=33646672646&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33646672646&partnerID=8YFLogxK

U2 - 10.1016/j.patrec.2005.07.018

DO - 10.1016/j.patrec.2005.07.018

M3 - Article

AN - SCOPUS:33646672646

VL - 27

SP - 1201

EP - 1208

JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

SN - 0167-8655

IS - 11

ER -