1. Paul Mann

2. Overview  Background  Outline of principles of methods  Limitations  Thoughts on design  Potential outputs  Variations  Any suggestions?

3. Background Last years symposium... I liked Kevin Dixon’s laser scanner, a toy I could get used to playing with. But most impressed by the potential of Anna Mason’s videogrammetry work.

4. Videogrammetry The low contrast problem... The method relies on tracking automatically identifiable features through a sequence of frames. Cave walls are often low contrast dull browns, lacking identifiable features. Shadows are the most readily identified feature, but these change position as the light source moves Suggested solution... Add targets But this takes time and is invasive. So why not use projected spots of light? Thinking time...

5. Trying out videogrammetry Freeware and demo versions of videogrammetry software on internet. Attempt to project light patterns Also enhance edge detection Plenty of difficulties, not many results

6. Sidelined “to build your own laser scanner for under a fiver...” 1. A computer 2. A web cam 3. A few sheets of paper 4. A laser 5. A glass fuse 6. David...

7. Success at last!

8. How it works...  Orthogonal background planes  Marked with a calibration grid Locates camera Scales and orientates planes Provides corrections for lens distortions  Structured light Planar laser  Calculate plane of light From incidence on orthogonal planes  Any other point lit must be on this plane And on linear ray path calculated for that pixel Hence point co-ordinates given by intersection of these two. Image from David-Laserscanner.com home page

9. Drawbacks  The David system is constrained by its need for two reference planes.  It is possible to scan though a hole to capture detail a behind the planes, but range is very limited as laser line must still intersect both planes.  Like most structured light solutions it is “inward” looking.

10. Useful in caves?  Excellent for capturing details on a cave wall Rock art Scallops  But can we capture the whole cave? Back to the drawing board

11. More internet research...  Excel 2009 survey trade show in York  Surveying is fun......it has lots of nice toys......but better to do it properly  So off to Glasgow...... project required

12. One solution  A structured light triangulation profiler

13. One solution  A structured light triangulation profiler

14. One solution  A structured light triangulation scanner

15. Design  The components: A planar laser A digital camera A suitably wide lens ○ A parabolic reflector lens is a bit pricey, but ideal. Fortunately I have a couple spare.

16. How it works Photo OUCC archives: F2, The Font Pitch, 1983

17. How it works.  Centre point equivalent to down  Fixed radius out is horizontal ie a circle  Lens tangent point at edge of circle About 70 degrees upwards

18. How it works..  Vertical angle (clino) is a function of distance from central point  Horizontal angle (compass) is directly equivalent to angle from central point

19. How it works...  Laser sheet is perpendicular to camera axis, and offset from lens by a measured distance.  School geometry Tan Φ = opposite adjacent Φ adjacent = offset x opposite = L, the distance from axis to point

20. Processing  First, find illuminated pixels  Each illuminated pixel has (X,Y) value Convert this to image polar co-ordinates (r,)  Convert image polar co-ordinates to instrument space polar co-ordinates (L, ) remains same Φ is function of r, L is a runction of Φ ie L is a function of r  Convert instrument polar co-ordinates into real space co-ordinates (x,y,z) Need to know orientation and position of instrument.

21. Complications to processing  Barrelling / pincushioning of camera lens Easily modelled  Modelling of parabolic lens curvature Again relatively easily modelled  Offset between axis of two lenses A bit more challenging  Either rigorous calibration to determine lens constants  Or skip and calibrate straight to a (X,Y) look up table

22. Limitations  Precision Limited by pixel spacing For 6MPixel camera: 1000 pixels cover 160 degrees vertically 1 pixel equates to about 1/6 degree For laser offset of 1m, and passage radius 1m this equates to 3mm precision But because of the tan Φ this drops off rapidly as passage radius increases.

23. Limitations  Is the drop off in precision a problem in cave survey situations? I suspect not In big passages, increase offset of laser  Need for low light Hard to make system work in daylight Suits cave survey

24. Output  Remember, not just one point being recorded – Typically a passage profile of 3000 points Captured at reshoot rate of camera Can easily distinguish red and green lasers Can easily determine above and below reflector plane Hence could create upwards of 10,000 points per shot Comparable to commercially available scanners

25. Challenges  Getting from a profiler to a scanner Movement need not be rotational, well suited for linear capture But how do we control such motion and record it accurately? Stringing taut wires a lot of work Shafts a lot easier to constrain Possibly active railway tunnels (or even recording vegetation overhanging lines)

26. Handling data  Pointclouds A set of points defined by their x,y,z coordinates May have other attributes linked (eg RGB)  Huge data sets  Specialist software  Getting away from paper

27. Variations  Other structured light solutions exist  How about combining this approach with photogrammetry / videogrammetry?  Increasing computer power is making complex solutions viable  Robotics is driving a lot of research in this area – worth keeping an eye on these developments

28. Over to you...  Thoughts about how this could be used, developed, etc very welcome.