Structure from motion point clouds for structural monitoring

Mistretta F.
First
;
Sanna G.
Second
;
Stochino F.
Penultimate
;
Vacca G.
Last
2019-01-01

Abstract

Dense point clouds acquired from Terrestrial Laser Scanners (TLS) have proved to be effective for structural deformation assessment. In the last decade, many researchers have defined methodology and workflow in order to compare different point clouds, with respect to each other or to a known model, assessing the potentialities and limits of this technique. Currently, dense point clouds can be obtained by Close-Range Photogrammetry (CRP) based on a Structure from Motion (SfM) algorithm. This work reports on a comparison between the TLS technique and the Close-Range Photogrammetry using the Structure from Motion algorithm. The analysis of two Reinforced Concrete (RC) beams tested under four-points bending loading is presented. In order to measure displacement distributions, point clouds at different beam loading states were acquired and compared. A description of the instrumentation used and the experimental environment, along with a comprehensive report on the calculations and results obtained is reported. Two kinds of point clouds comparison were investigated: Mesh to mesh and modeling with geometric primitives. The comparison between the mesh to mesh (m2m) approach and the modeling (m) one showed that the latter leads to significantly better results for both TLS and CRP. The results obtained with the TLS for both m2m and m methodologies present a Root Mean Square (RMS) levels below 1 mm, while the CRP method yields to an RMS level of a few millimeters for m2m, and of 1 mm for m.
2019
2019
Inglese
11
16
1940
20
https://res.mdpi.com/d_attachment/remotesensing/remotesensing-11-01940/article_deploy/remotesensing-11-01940.pdf
Esperti anonimi
internazionale
scientifica
Close-range photogrammetry; Point clouds modeling; Structural health monitoring; Structure from motion; Terrestrial laser scanner
no
Mistretta, F.; Sanna, G.; Stochino, F.; Vacca, G.
1.1 Articolo in rivista
info:eu-repo/semantics/article
1 Contributo su Rivista::1.1 Articolo in rivista
262
4
open
Files in This Item:
File Size Format  
Mistretta_Sanna_Stochino_Vacca_RemoteSensing2019.pdf

open access

Type: versione editoriale
Size 6.61 MB
Format Adobe PDF
6.61 MB Adobe PDF View/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Questionnaire and social

Share on:
Impostazioni cookie