AUTOMATED COARSE REGISTRATION of POINT CLOUDS in 3D URBAN SCENES USING VOXEL BASED PLANE CONSTRAINT

Y. Xu, R. Boerner, Wei Yao, L. Hoegner, U. Stilla

Research output: Journal article publicationConference articleAcademic researchpeer-review

24 Citations (Scopus)

Abstract

For obtaining a full coverage of 3D scans in a large-scale urban area, the registration between point clouds acquired via terrestrial laser scanning (TLS) is normally mandatory. However, due to the complex urban environment, the automatic registration of different scans is still a challenging problem. In this work, we propose an automatic marker free method for fast and coarse registration between point clouds using the geometric constrains of planar patches under a voxel structure. Our proposed method consists of four major steps: the voxelization of the point cloud, the approximation of planar patches, the matching of corresponding patches, and the estimation of transformation parameters. In the voxelization step, the point cloud of each scan is organized with a 3D voxel structure, by which the entire point cloud is partitioned into small individual patches. In the following step, we represent points of each voxel with the approximated plane function, and select those patches resembling planar surfaces. Afterwards, for matching the corresponding patches, a RANSAC-based strategy is applied. Among all the planar patches of a scan, we randomly select a planar patches set of three planar surfaces, in order to build a coordinate frame via their normal vectors and their intersection points. The transformation parameters between scans are calculated from these two coordinate frames. The planar patches set with its transformation parameters owning the largest number of coplanar patches are identified as the optimal candidate set for estimating the correct transformation parameters. The experimental results using TLS datasets of different scenes reveal that our proposed method can be both effective and efficient for the coarse registration task. Especially, for the fast orientation between scans, our proposed method can achieve a registration error of less than around 2 degrees using the testing datasets, and much more efficient than the classical baseline methods.
Original languageEnglish
Pages (from-to)185-191
Number of pages7
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume4
Issue number2W4
DOIs
Publication statusPublished - 12 Sep 2017
Externally publishedYes
EventISPRS Geospatial Week 2017 - Wuhan, China
Duration: 18 Sep 201722 Sep 2017

Keywords

  • Coarse registration
  • Plane constraint
  • Point cloud
  • RANSAC
  • Voxel structure

ASJC Scopus subject areas

  • Earth and Planetary Sciences (miscellaneous)
  • Environmental Science (miscellaneous)
  • Instrumentation

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