Fast and robust isotropic scaling iterative closest point algorithm

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

The iterative closest point (ICP) algorithm is an accurate approach for the registration between two point sets on the same scale. However, it can not handle the case with different scales. This paper proposes a fast and robust ICP algorithm for isotropic scaling point sets registration (FRISICP). In order to accurately and directly estimate the scale factor without any constraints, we introduce a bidirection distance measurement method into the least square (LS) problem. Then to keep computational efficiency when the number of points in the set increasing, we further introduce a sparse-to-dense hierarchical model in ICP algorithm to speed up the isotropic scaling point set matching process. Experimental results demonstrate that the proposed FRISICP method outperforms other algorithms on both 2D and 3D point sets.

Original languageEnglish
Title of host publicationICIP 2011
Subtitle of host publication2011 18th IEEE International Conference on Image Processing
Pages1485-1488
Number of pages4
DOIs
StatePublished - 2011
Event2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium
Duration: 11 Sep 201114 Sep 2011

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference2011 18th IEEE International Conference on Image Processing, ICIP 2011
Country/TerritoryBelgium
CityBrussels
Period11/09/1114/09/11

Keywords

  • Bidirection distance measurement
  • ICP
  • Isotropic scaling registration
  • Sparse-to-dense

Fingerprint

Dive into the research topics of 'Fast and robust isotropic scaling iterative closest point algorithm'. Together they form a unique fingerprint.

Cite this