Color point cloud registration with 4D ICP algorithm

Hao Men, Biruk Gebre, Kishore Pochiraju

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

114 Scopus citations

Abstract

This paper presents methodologies to accelerate the registration of 3D point cloud segments by using hue data from the associated imagery. The proposed variant of the Iterative Closest Point (ICP) algorithm combines both normalized point range data and weighted hue value calculated from RGB data of an image registered 3D point cloud. A k-d tree based nearest neighbor search is used to associated common points in {x, y, z, hue} 4D space. The unknown rigid translation and rotation matrix required for registration is iteratively solved using Singular Value Decomposition (SVD) method. A mobile robot mounted scanner was used to generate color point cloud segments over a large area. The 4D ICP registration has been compared with typical 3D ICP and numerical results on the generated map segments shows that the 4D method resolves ambiguity in registration and converges faster than the 3D ICP.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Robotics and Automation, ICRA 2011
Pages1511-1516
Number of pages6
DOIs
StatePublished - 2011
Event2011 IEEE International Conference on Robotics and Automation, ICRA 2011 - Shanghai, China
Duration: 9 May 201113 May 2011

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2011 IEEE International Conference on Robotics and Automation, ICRA 2011
Country/TerritoryChina
CityShanghai
Period9/05/1113/05/11

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