Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5, 293-299, 2014
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-5/293/2014/
doi:10.5194/isprsarchives-XL-5-293-2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
06 Jun 2014
Scaling up close-range surveys, a challenge for the generalization of as-built data in industrial applications
J.-F. Hullo and G. Thibault EDF R&D, SINETICS, 92140 Clamart, France
Keywords: As-built data, terrestrial laser scanner, panoramic images, CAD models, industrial applications, large scale scanning surveys Abstract. As-built CAD data reconstructed from Terrestrial Laser Scanner (TLS) data are used for more than two decades by Electricité de France (EDF) to prepare maintenance operations in its facilities. But today, the big picture is renewed: "as-built virtual reality" must address a huge scale-up to provide data to an increasing number of applications. In this paper, we first present a wide multi-sensor multi-purpose scanning campaign performed in a 10 floor building of a power plant in 2013: 1083 TLS stations (about 40.109 3D points referenced under a 2 cm tolerance) and 1025 RGB panoramic images (340.106 pixels per point of view). As expected, this very large survey of high precision measurements in a complex environment stressed sensors and tools that were developed for more favourable conditions and smaller data sets. The whole survey process (tools and methods used from acquisition and processing to CAD reconstruction) underwent a detailed follow-up in order to state on the locks to a possible generalization to other buildings. Based on these recent feedbacks, we have highlighted some of these current bottlenecks in this paper: sensors denoising, automation in processes, data validation tools improvements, standardization of formats and (meta-) data structures.
Conference paper (PDF, 1119 KB)


Citation: Hullo, J.-F. and Thibault, G.: Scaling up close-range surveys, a challenge for the generalization of as-built data in industrial applications, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5, 293-299, doi:10.5194/isprsarchives-XL-5-293-2014, 2014.

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