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Automated Reconstruction of Industrial Sites

Date post: 02-Jan-2016
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Automated Reconstruction of Industrial Sites. Frank van den Heuvel Tahir Rabbani. Overview. Introduction Automation : how does it work? Sample project off-shore platform Accuracy Future Conclusions. The group Photogrammetry & Remote Sensing. - PowerPoint PPT Presentation
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Automated Reconstruction of Industrial Sites Frank van den Heuvel Tahir Rabbani
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Page 1: Automated Reconstruction of Industrial Sites

Automated Reconstruction of Industrial Sites

Frank van den HeuvelTahir Rabbani

Page 2: Automated Reconstruction of Industrial Sites

Overview

• Introduction• Automation: how does it work?• Sample project off-shore platform• Accuracy• Future• Conclusions

Page 3: Automated Reconstruction of Industrial Sites

The groupPhotogrammetry & Remote Sensing• “Development of efficient techniques for the

acquisition of 3D information by computer-assisted analysis of image and range data“

Page 4: Automated Reconstruction of Industrial Sites

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

Page 5: Automated Reconstruction of Industrial Sites

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”

Page 6: Automated Reconstruction of Industrial Sites

Automated reconstruction procedureOverview (1/3)• Segmentation• Grouping points of surface patches

Page 7: Automated Reconstruction of Industrial Sites

Automated reconstruction procedureOverview (2/3)• Segmentation• Grouping points of surface patches

• Object Detection• Finding planes and cylinders

Page 8: Automated Reconstruction of Industrial Sites

Automated reconstruction procedureOverview (3/3)• Segmentation• Grouping points of surface patches

• Object Detection• Finding planes and cylinders

• Fitting• Final parameter estimation

Page 9: Automated Reconstruction of Industrial Sites

Segmentation – step 1

• Estimation of surface normals using K-nearest neighbours (here K=10 points)

Page 10: Automated Reconstruction of Industrial Sites

Segmentation – step 2

• Region growing using:• Connectivity (K-nearest

neighbours) • Surface smoothness

(angle between normals)

Page 11: Automated Reconstruction of Industrial Sites

Detection – Planes

• Plane detection using Hough transform• Find orientation as maximum on Gaussian

sphere

Page 12: Automated Reconstruction of Industrial Sites

Detection – Cylinders

• Cylinder detection using Hough transform in 2 steps:• Step 1: Orientation

Page 13: Automated Reconstruction of Industrial Sites

Detection – Cylinders

• Cylinder detection using Hough transform in 2 steps:• Step 1: Orientation

Page 14: Automated Reconstruction of Industrial Sites

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

Page 15: Automated Reconstruction of Industrial Sites

Example: detection of two cylinders

• Point cloud segment

Page 16: Automated Reconstruction of Industrial Sites

Example: detection of two cylinders

• Surface normals

Page 17: Automated Reconstruction of Industrial Sites

Example: detection of two cylinders

• Normals on Gaussian sphere

Page 18: Automated Reconstruction of Industrial Sites

Example: detection of two cylinders

• Orientation of first cylinder (next: position)

Page 19: Automated Reconstruction of Industrial Sites

Example: detection of two cylinders

• Remove first cylinder points from segment

Page 20: Automated Reconstruction of Industrial Sites

Example: detection of two cylinders

• Procedure repeated for second cylinder

Page 21: Automated Reconstruction of Industrial Sites

Example: detection of two cylinders

• Result: two detected cylinders

Page 22: Automated Reconstruction of Industrial Sites

Fitting

• Complete CSG model + constraint specification

• Final least-squares parameter estimation of CSG model

Page 23: Automated Reconstruction of Industrial Sites

Fitting

• Final least-squares parameter estimation of CSG model• Minimise sum of squared distances• Enforce constraints

Page 24: Automated Reconstruction of Industrial Sites

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

Page 25: Automated Reconstruction of Industrial Sites

Results on platform modelling

Page 26: Automated Reconstruction of Industrial Sites

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

Page 27: Automated Reconstruction of Industrial Sites

0 2 4 6 8 10 12 14 16 18 200

10

20

30

40

50

60

70

80

90

100

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

Page 28: Automated Reconstruction of Industrial Sites

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

Page 29: Automated Reconstruction of Industrial Sites

Accuracy

• Object deformation or segmentation limitations?

Fitting after initial segmentation

Max.residual 21 mm

Fitting after rejecting large residuals

Max. residual 9 mm

Page 30: Automated Reconstruction of Industrial Sites

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

Page 31: Automated Reconstruction of Industrial Sites

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

Page 32: Automated Reconstruction of Industrial Sites

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?)

Page 33: Automated Reconstruction of Industrial Sites

Future – integration with imagery

Page 34: Automated Reconstruction of Industrial Sites

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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