Multi-Sensor As-Built Models of Complex Industrial Architectures

Hullo, Jean-Francois; Thibault, Guillaume; Boucheny, Christian; Dory, Fabien; Mas, Arnaud

doi:10.3390/rs71215827

Cited by 34 publications

(22 citation statements)

References 28 publications

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“…However, they also can be applied in industrial context and energy applications [1][2][3][4]. In this way, geomatics techniques can be used to generate three-dimensional models of large and complex scenes as nuclear power plants [5], the generation of as-built models in architecture [6,7] or, even, for metrological tasks related with the quality assessment of products [8][9][10][11]. Portable mobile mapping systems (PMMSs) allow the generation of dense, geo-referenced, three-dimensional models while the operator is moving though the scene [12].…”

Section: Introductionmentioning

confidence: 99%

Validation of Portable Mobile Mapping System for Inspection Tasks in Thermal and Fluid–Mechanical Facilities

et al. 2019

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The three-dimensional registration of industrial facilities has a great importance for maintenance, inspection, and safety tasks and it is a starting point for new improvements and expansions in the industrial facilities context. In this paper, a comparison between the results obtained using a novel portable mobile mapping system (PMMS) and a static terrestrial laser scanner (TLS), widely used for 3D reconstruction in civil and industrial scenarios, is carried out. This comparison is performed in the context of industrial inspection tasks, specifically in the thermal and fluid-mechanics facilities in a hospital. The comparison addresses the general reconstruction of a machine room, focusing on the quantitative and qualitative analysis of different elements (e.g., valves, regulation systems, burner systems and tanks, etc.). The validation of the PMMS is provided considering the TLS as ground truth and applying a robust statistical analysis. Results come to confirm the suitability of the PMMS to perform inspection tasks in industrial facilities.

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Section: Introductionmentioning

confidence: 99%

Validation of Portable Mobile Mapping System for Inspection Tasks in Thermal and Fluid–Mechanical Facilities

et al. 2019

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“…In the case of recordings of cluttered indoor scenes, cloud-to-cloud methods are difficult to use for topographical purposes, both due to their high calculation consumption rate and to their lack of robustness. For the registration such as for the referencing of large networks, the judicious arrangement of targets in the environment remains a very productive method for ensuring the centimetric accuracy ranges (Hullo et al, 2015). Cloud-to-cloud constraints can nevertheless still be implemented in case of a failed use of the targets (manual segmentation of connected sub-clouds or refinement steps between station pairs).…”

Section: State Of the Artmentioning

confidence: 99%

“…This need is specified geometrically with accuracy, precision and reliability criteria, which are subject to a quality control which may be demanding, for example, in the industry where centimetric precisions at the scale of a building can be required (Hullo et al, 2015). In order to ensure these criteria as well as to allow the detection and investigation of defects, it is necessary to use relevant indicators and to establish a set of checks throughout the data production process.…”

Section: Principles Of Evaluationmentioning

confidence: 99%

Fine Registration of Kilo-Station Networks - A Modern Procedure for Terrestrial Laser Scanning Data Sets

Hullo¹

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:We propose a complete methodology for the fine registration and referencing of kilo-station networks of terrestrial laser scanner data currently used for many valuable purposes such as 3D as-built reconstruction of Building Information Models (BIM) or industrial asbuilt mock-ups. This comprehensive target-based process aims to achieve the global tolerance below a few centimetres across a 3D network including more than 1,000 laser stations spread over 10 floors. This procedure is particularly valuable for 3D networks of indoor congested environments. In situ, the use of terrestrial laser scanners, the layout of the targets and the set-up of a topographic control network should comply with the expert methods specific to surveyors. Using parametric and reduced Gauss-Helmert models, the network is expressed as a set of functional constraints with a related stochastic model. During the post-processing phase inspired by geodesy methods, a robust cost function is minimised. At the scale of such a data set, the complexity of the 3D network is beyond comprehension. The surveyor, even an expert, must be supported, in his analysis, by digital and visual indicators. In addition to the standard indicators used for the adjustment methods, including Baarda's reliability, we introduce spectral analysis tools of graph theory for identifying different types of errors or a lack of robustness of the system as well as in fine documenting the quality of the registration.

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“…Yu et al [12] identify road features from mobile terrestrial LiDAR datasets. Hullo et al [13] create as-built model of industrial sites from terrestrial LiDAR data. Fang et al [14] extract trees from airborne LiDAR point clouds.…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced Algorithm for Concurrent Recognition of Rail Tracks and Power Cables from Terrestrial and Airborne LiDAR Point Clouds

Arastounia

2017

Infrastructures

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This study proposes an enhanced algorithm that outperforms the methods developed by the author's earlier contributions for the recognition of railroad assets from LiDAR point clouds. The algorithm is improved by: (1) making it applicable to railroads with any slope; (2) employing Eigen decomposition for the rail seed point selection that makes it independent of the rails' dimensions; and (3) developing a computationally efficient fully data-driven method (simultaneous identification of rail tracks and contact cables) that is able to process poorly sampled datasets with complicated configurations. The upgraded algorithm is applied to two datasets with quite different point sampling and complexity. First dataset is scanned by a terrestrial system and contains three million points covering 630 m of an inter-city railroad corridor. It presents a simple configuration with nonintersecting straight rail tracks and cables. Second dataset includes 80 m of a complex urban railroad environment comprising curved and merging rail tracks and intersecting cables. It is scanned from an airborne platform and contains 165,000 points. The results indicate that all objects of interest are identified and the average recognition precision and accuracy of both datasets at the point cloud level are greater than 95%.

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Multi-Sensor As-Built Models of Complex Industrial Architectures

Cited by 34 publications

References 28 publications

Validation of Portable Mobile Mapping System for Inspection Tasks in Thermal and Fluid–Mechanical Facilities

Validation of Portable Mobile Mapping System for Inspection Tasks in Thermal and Fluid–Mechanical Facilities

Fine Registration of Kilo-Station Networks - A Modern Procedure for Terrestrial Laser Scanning Data Sets

An Enhanced Algorithm for Concurrent Recognition of Rail Tracks and Power Cables from Terrestrial and Airborne LiDAR Point Clouds

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