Consolidation de relevés laser d'intérieurs construits : pour une approche probabiliste initialisée par géolocalisation

Abstract : Many pre-maintenance operations of industrial facilities currently resort on to three-dimensional CAD models. The acquisition of these models is performed from point clouds measured by Terrestrial Laser Scanning (TLS). When the scenes are complex, several viewpoints for scanning, also known as stations, are necessary to ensure the completeness and the density of the survey data. The generation of a global point cloud, i.e. the expression of all the acquired data in a common reference frame, is a crucial step called registration. During this process, the pose parameters are estimated. If the GNSS systems are now a solution for many outdoor scenes, the registration of indoor TLS data still remains a challenge. The objective of this thesis is to improve the acquisition process of TLS data in industrial environments. The aim is to guarantee the precision and accuracy of acquired data, while optimizing on-site acquisition time and protocols by, as often as possible, freeing the operator from the constraints inherent to conventional topographic surveys. In a first part, we consider the state of the art of the means and methods used during the acquisition of dense point clouds of complex interior scenes (Part I). In a second part, we study and evaluate the data available for the registration: terrestrial laser scanner data, primitive reconstruction algorithms in point clouds and indoor geolocation systems (Part II). In the third part, we then formalize and experiment a registration algorithm based on the use of matched primitives, reconstructed from per station point clouds (Part III). We finally propose a probabilistic approach for matching primitives, allowing the integration of a priori information and uncertainty in the constraints system used for calculating poses (Part IV).
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Submitted on : Monday, March 18, 2013 - 5:32:15 PM
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Jean-Francois Hullo. Consolidation de relevés laser d'intérieurs construits : pour une approche probabiliste initialisée par géolocalisation. Autre. Université de Strasbourg, 2013. Français. ⟨NNT : 2013STRAD001⟩. ⟨tel-00801974⟩

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