1. Automated Reconstruction of Industrial Sites Frank van den Heuvel Tahir Rabbani

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

3. The group Photogrammetry & Remote Sensing “Development of efficient techniques for the acquisition of 3D information by computer-assisted analysis of image and range data“

4. The project Services 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

5. The project Services and Training through Augmented Reality (STAR) Partners: Siemens, KULeuven, EPFL, UNIGE, Realviz TUDelft: “Automated 3D reconstruction of industrial installations from laser and image data”

6. Automated reconstruction procedure Overview (1/3) Segmentation Grouping points of surface patches

7. Automated reconstruction procedure Overview (2/3) Segmentation Grouping points of surface patches Object Detection Finding planes and cylinders

8. Automated reconstruction procedure Overview (3/3) Segmentation Grouping points of surface patches Object Detection Finding planes and cylinders Fitting Final parameter estimation

9. Segmentation – step 1 Estimation of surface normals using K-nearest neighbours (here K=10 points)

10. Segmentation – step 2 Region growing using: Connectivity (K-nearest neighbours) Surface smoothness (angle between normals)

11. Detection – Planes Plane detection using Hough transform Find orientation as maximum on Gaussian sphere

12. Detection – Cylinders Cylinder detection using Hough transform in 2 steps: Step 1: Orientation

13. Detection – Cylinders Cylinder detection using Hough transform in 2 steps: Step 1: Orientation

14. 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

15. Example: detection of two cylinders Point cloud segment

16. Example: detection of two cylinders Surface normals

17. Example: detection of two cylinders Normals on Gaussian sphere

18. Example: detection of two cylinders Orientation of first cylinder (next: position)

19. Example: detection of two cylinders Remove first cylinder points from segment

20. Example: detection of two cylinders Procedure repeated for second cylinder

21. Example: detection of two cylinders Result: two detected cylinders

22. Fitting Complete CSG model + constraint specification Final least-squares parameter estimation of CSG model

23. Fitting Final least-squares parameter estimation of CSG model Minimise sum of squared distances Enforce constraints

24. 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

25. Results on platform modelling

26. Results on platform modelling Statistics 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 parameters9798 500 Mb to 0.1 Mb

27. Results on platform modelling Accuracy Residual analysis: RMS: 4.3 mm 83% < 5 mm 96% < 10 mm

28. 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

29. Accuracy Object deformation or segmentation limitations? Fitting after initial segmentation Max.residual 21 mm Fitting after rejecting large residuals Max. residual 9 mm

30. 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

31. 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

32. 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?)What is there

33. Future – integration with imagery

34. 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