Consistent point clouds of narrow dpaces using multiscan domain mapping

  1. Get@NRC: Consistent point clouds of narrow dpaces using multiscan domain mapping (Opens in a new window)
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Journal titleComputer-Aided Civil and Infrastructure Engineering
Pages555572; # of pages: 18
SubjectCivil structure; Data approximation; Data density; Data-acquisition devices; Density variations; Domain mapping; Experimental observation; Incident angles; Inconsistent data; Large spaces; Laser scanner; Limited data; Multi-scan; Partial scan; Planning methodology; Point cloud; Point-based visualization; Scanning range; Stationary positions; Scanning; Three dimensional; Visualization; Data visualization; accuracy assessment; civil engineering; data acquisition; experimental study; observational method; structural analysis; three-dimensional modeling; visualization
AbstractThree-dimensional (3D) range scanning of large spaces, such as civil structures, generates an immense cloud of 3D points with inconsistent data densities due to the limited positions of the stationary scanner, inaccessible surfaces, and narrow pathways. This density variation is the dominant detrimental factor in extracting accurate scanned shapes. This article introduces an effective scan planning methodology for capturing accurate geometry from long and narrow spaces, which minimizes the need for subsequent data approximations. The technique computes an optimum scanning range for each stationary position of the scanner that limits the density variation to a user-defined value. Three cases are proposed to define the "limited data density" and a FARO®-LS880 laser scanner is used to illustrate the proposed approach that achieves acceptable scanning results in terms of its critical shape capturing capability, overall point cloud density, and accurate point-based visualization. The experimental observations confirm that the accuracy of the scanned data can be improved by registering multiple partial scans with restricted density and positioning the data acquisition device close to the critical features. The latter recommended step decreases the incident angle to the world domain, which, in turn, reduces the surface occlusions and data density variations. © 2011 Computer-Aided Civil and Infrastructure Engineering.
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AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Research in Construction (IRC-IRC)
Peer reviewedYes
NPARC number21269355
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Record identifier82db7e20-499e-48aa-8268-5a25b7c39cba
Record created2013-12-12
Record modified2016-05-09
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