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Automated Recognition of 3D CAD Model Objects in Dense Laser Range Point Clouds

There is shift in the Architectural / Engineering / Construction and Facility Management (AEC&FM) industry toward performance-driven projects. Assuring
good performance requires efficient and reliable performance control processes.
However, the current state of the AEC&FM industry is that control processes are
inefficient because they generally rely on manually intensive, inefficient, and often
inaccurate data collection techniques.
Critical performance control processes include progress tracking and dimensional
quality control. These particularly rely on the accurate and efficient collection
of the as-built three-dimensional (3D) status of project objects. However, currently available
techniques for as-built 3D data collection are extremely inefficient, and provide
partial and often inaccurate information. These limitations have a negative impact
on the quality of decisions made by project managers and consequently on project
success.
This thesis presents an innovative approach for Automated 3D Data Collection
(A3dDC). This approach takes advantage of Laser Detection and Ranging
(LADAR), 3D Computer-Aided-Design (CAD) modeling and registration technologies.
The performance of this approach is investigated with a first set of experimental
results obtained with real-life data. A second set of experiments then
analyzes the feasibility of implementing, based on the developed approach, automated
project performance control (APPC) applications such as automated project
progress tracking and automated dimensional quality control. Finally, other applications
are identified including planning for scanning and strategic scanning.

Identiferoai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/3849
Date January 2008
CreatorsBosche, Frederic
Source SetsUniversity of Waterloo Electronic Theses Repository
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation

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