Geometric point quality assessment for the automated, markerless and robust registration of unordered tls point clouds

M. Weinmann, B. Jutzi

Research output: Contribution to journalConference articlepeer-review

18 Scopus citations

Abstract

The faithful 3D reconstruction of urban environments is an important prerequisite for tasks such as city modeling, scene interpretation or urban accessibility analysis. Typically, a dense and accurate 3D reconstruction is acquired with terrestrial laser scanning (TLS) systems by capturing several scans from different locations, and the respective point clouds have to be aligned correctly in a common coordinate frame. In this paper, we present an accurate and robust method for a keypoint-based registration of unordered point clouds via projective scan matching. Thereby, we involve a consistency check which removes unreliable feature correspondences and thus increases the ratio of inlier correspondences which, in turn, leads to a faster convergence of the RANSAC algorithm towards a suitable solution. This consistency check is fully generic and it not only favors geometrically smooth object surfaces, but also those object surfaces with a reasonable incidence angle. We demonstrate the performance of the proposed methodology on a standard TLS benchmark dataset and show that a highly accurate and robust registration may be achieved in a fully automatic manner without using artificial markers.

Original languageEnglish
Pages (from-to)89-96
Number of pages8
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume2
Issue number3W5
DOIs
StatePublished - 19 Aug 2015
Externally publishedYes
EventISPRS Geospatial Week 2015 - La Grande Motte, France
Duration: 28 Sep 20153 Oct 2015

Keywords

  • Feature extraction
  • Filtering
  • Imagery
  • Laser scanning
  • Matching
  • Point cloud
  • Registration
  • TLS

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