automated reconstruction of industrial sites frank van den heuvel tahir rabbani
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Automated Reconstruction of Industrial Sites
Frank van den HeuvelTahir Rabbani
Overview
• Introduction• Automation: how does it work?• Sample project off-shore platform• Accuracy• Future• Conclusions
The groupPhotogrammetry & Remote Sensing• “Development of efficient techniques for the
acquisition of 3D information by computer-assisted analysis of image and range data“
The projectServices and Training through Augmented Reality (STAR)
• EU fifth framework – IST programme• “Develop new Augmented Reality techniques
for training, on-line documentation, maintenance and planning purposes in industrial applications”
• AR-example: virtual human in video
The projectServices and Training through Augmented Reality (STAR)
• Partners: Siemens, KULeuven, EPFL, UNIGE, Realviz
• TUDelft: “Automated 3D reconstruction of industrial installations from laser and image data”
Automated reconstruction procedureOverview (1/3)• Segmentation• Grouping points of surface patches
Automated reconstruction procedureOverview (2/3)• Segmentation• Grouping points of surface patches
• Object Detection• Finding planes and cylinders
Automated reconstruction procedureOverview (3/3)• Segmentation• Grouping points of surface patches
• Object Detection• Finding planes and cylinders
• Fitting• Final parameter estimation
Segmentation – step 1
• Estimation of surface normals using K-nearest neighbours (here K=10 points)
Segmentation – step 2
• Region growing using:• Connectivity (K-nearest
neighbours) • Surface smoothness
(angle between normals)
Detection – Planes
• Plane detection using Hough transform• Find orientation as maximum on Gaussian
sphere
Detection – Cylinders
• Cylinder detection using Hough transform in 2 steps:• Step 1: Orientation
Detection – Cylinders
• Cylinder detection using Hough transform in 2 steps:• Step 1: Orientation
Detection – Cylinders
• Cylinder detection using Hough transform in 2 steps:• Step 1: Orientation (2 parameters)• Step 2: Position and Radius (3 parameters)
u,v search space at correct Radius
Example: detection of two cylinders
• Point cloud segment
Example: detection of two cylinders
• Surface normals
Example: detection of two cylinders
• Normals on Gaussian sphere
Example: detection of two cylinders
• Orientation of first cylinder (next: position)
Example: detection of two cylinders
• Remove first cylinder points from segment
Example: detection of two cylinders
• Procedure repeated for second cylinder
Example: detection of two cylinders
• Result: two detected cylinders
Fitting
• Complete CSG model + constraint specification
• Final least-squares parameter estimation of CSG model
Fitting
• Final least-squares parameter estimation of CSG model• Minimise sum of squared distances• Enforce constraints
Results on platform modelling
• Scanned by Delftech in 2003• Subset of 17.7 million points used by TUD:• Automated detection of 2338 objects• R.M.S. of residuals 4.3 mm
Results on platform modelling
Results on platform modellingStatistics
• Points: 17.7 million• Points in segments: 14.2 million(80%)• Points on objects:9.3 million (53%)• Detected:• Planar patches: 946• Cylinders: 1392
• Data reduction:• Object parameters 9798• 500 Mb to 0.1 Mb
0 2 4 6 8 10 12 14 16 18 200
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Residual (mm)
%ag
e of
poi
nts
Cumulative histogram of residuals
Results on platform modelling Accuracy• Residual analysis:
• RMS: 4.3 mm• 83% < 5 mm• 96% < 10 mm
Accuracy
• Data precision:• Scanner: 6 mm (averaging: 3 mm)
• Scanner dependent
• Model precision:• Discrepancies models - real world: 0.1-10 mm ?
• Limited production accuracy• Deformations• Imperfections in segmentation
Accuracy
• Object deformation or segmentation limitations?
Fitting after initial segmentation
Max.residual 21 mm
Fitting after rejecting large residuals
Max. residual 9 mm
Future – automation
• Reconstruction using laser data:• Segmentation, primitive detection (available)• Correspondence between primitives >
registration• Model improvement:
• Constraint detection (piping structure)• Recognition of complex elements in a database
• Integration with digital imagery
Future – integration with imagery
• Instrumentation developments• Scanners with integrated high-resolution digital
camera
• Accuracy improvement• Imagery complementary: Laser for surfaces, image for
edges• Integrated fitting of models to laser and image data
Future – integration with imagery
• Instrumentation developments• Scanners with integrated high-resolution camera
• Accuracy improvement• Imagery complementary: Laser for surfaces, image for
edges• Integrated fitting of models to laser and image data
• Flexibility of image acquisition: Completeness• Non-geometric information (What is there?)
Future – integration with imagery
Conclusions
• Bright future for automation using laser data• More research to be done:• Automated registration• Integration with digital imagery• Using domain knowledge for automated
modelling:• Closer connection to the model users needed:• Domain knowledge for automation• Utilisation of research results
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