POINT CLOUD REGISTRATION: CURRENT STATE OF THE SCIENCE. Matthew P. Tait

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1 POINT CLOUD REGISTRATION: CURRENT STATE OF THE SCIENCE Matthew P. Tait

2 Content 1. Quality control: Analyzing the true errors in Terrestrial Laser Scanning (TLS) 2. The prospects for automatic cloud registration in engineering measurement

3 Quality control: Analyzing the true errors in Terrestrial Laser Scanning (TLS) Question. This end cap need to be located to an accuracy of 1cm (95% confidence) relative to a point 100m away. Can you prove your position is to this accuracy (whether it is or not)?

4 Quality control: Analyzing the true errors in Terrestrial Laser Scanning (TLS) Precision versus Accuracy These are only the same if all random error is correctly propagated and no biases are present

5 Quality control: Analyzing the true errors in Terrestrial Laser Scanning (TLS) Estimated Position (deterministic) Estimated Position Error True Position (never known) (Statistical) Question becomes: Does the 95% confidence error estimate cover the true position?

6 Quality control: Analyzing the true errors in Terrestrial Laser Scanning (TLS) Answer: Yes, if the error sources are correctly combined to achieve the estimated error Is this being done?

7 Error in TLS Survey Network Targets Calibration Self-calibrating bundle adjustment Atmospheric Registration TLS Modeling

8 Error in TLS Survey Network Target estimation = 1mm standard deviation 20m

9 Error in TLS Registration

10 Quality control: Analyzing the true errors in Terrestrial Laser Scanning (TLS) Estimated Position (deterministic) Estimated Position Error (Statistical) True Position (known to ±2.5mm 95% confidence) The 95% confidence ellipse does not cover the true location

11 Is Benchmarking the answer? Survey Network 1 Targets 1 Calibration 1 Atmospheric 1 Registration 1 Modeling 1 Survey Network 2 Targets 2 Calibration 2 Atmospheric 2 Registration 2 Modeling 2

12 Example of propagating errors 1/3 Errors included Survey Network Instrument errors 13 scans Riegl LMS-Z210 Derek Lichti, Stuart Gordon, and Taravudh Tipdecho. (2005). Error models and propagation in directly georeferenced TLS networks. J. of Eng. Surv.

13 Example of propagating errors 2/3

14 Example of propagating errors 3/3 90% errors < 100mm (95%) Stated precision ±50mm (95%) range observation Mostly due to beam divergence and vertical angle errors

15 Automatic cloud registration in engineering measurement Principles for fine registration ICP, Iterative Closest Point (and derivatives / extensions) Cloud 1 Cloud 2 100% correspondence of points

16 Automatic cloud registration in engineering measurement Principles of coarse registration Reducing the search space (splines, curvature changes, geometric primitives, gaussian spheres etc. etc.) Cloud 1 Cloud 2 100% correspondence of points

17 Automatic cloud registration in engineering measurement coarse registration + fine registration = automatic registration? Problem 1 To the best of the authors knowledge, a method for the registration of partially overlapping point clouds from TLS without good a priori alignment has not developed yet Kwang-Ho and Lichti. (2004). Automated registration of unorganized point clouds from terrestrial laser scanners. ISPRS Istanbul

18 Automatic cloud registration in engineering measurement coarse registration + fine registration = automatic registration? Problem 2 Matching non-rigidly-deforming clouds Problem 3 Internal quality control Gruen and Acka. (2005). Least squares 3D surface and curve matching. ISPRS Journal of Photogrammetry and Remote Sensing

19 Automatic cloud registration in engineering measurement Problem 1 Initial Orientation Disorganized data? Kwang-Ho and Lichti. (2004). Automated registration of unorganized point clouds from terrestrial laser scanners. ISPRS Istanbul

20 Automatic cloud registration in engineering measurement Problem 1 Quality of corresponding data? Dold. (2005). Extended gaussian images for the registration of terrestrial scan data. ISPRS Enschede

21 Automatic cloud registration in engineering measurement Problem 1 In engineering measurement I have: Clouds with large angular differences Clouds with relatively low # of corresponding points Tait and Fox. (2004). A comparison and full error budget analysis for close-range photogrammetry and laser scanning in industrial environments. INGEO2004

22 Automatic cloud registration in engineering measurement Problem 2 Non-rigidly deforming clouds Most matching strategies give a one-number fit estimate Large registered clouds vary in the quality of registration = Problem 3 Cloud 1 Cloud 2 100% correspondence of points

23 Automatic cloud registration in engineering measurement Solution 1 Throw computing power at it Time consuming and questionable relative accuracies over the result Solution 2 Initial orientation using primitives Propagation of point error Least Squares surface matching

24 Automatic cloud registration in engineering measurement Initial Orientation using primitives 1/2 85% industrial objects can be approximated by: Planes Spheres Cones Cylinders 95% if toroidal surfaces are included Rabbani and Van Der Heuvel. (2005). Efficient Hough transform for automatic detection of cylinders in point clouds. ISPRS Enschede.

25 Automatic cloud registration in engineering measurement Initial Orientation using primitives 1/2 Rabbani and Van Der Heuvel. (2005). Efficient Hough transform for automatic detection of cylinders in point clouds. ISPRS Enschede. The Hough transform is a technique which can be used to isolate features of a particular shape within an image.

26 Automatic cloud registration in engineering measurement Rigorous propagation of point errors Requires further work in sensor calibration and atmospheric correction This is currently being looked at by many workers in universities Models already exist that can be integrated into workflow

27 Automatic cloud registration in engineering measurement Least Squares Matching of Surfaces Propagates error properly in different parts of the cloud registration Warns if areas are poorly determined Example: 19 scans, 78 million points, reliable estimate of fit (all points involved) at 1.5cm level Acka. (2005). Co-registration of large volume laser scanner point clouds: The Pichango Alto (Peru) data set. Internal Technical Report. ETH Zurich

28 Automatic cloud registration in engineering measurement Quoted advantages of cloud-cloud registration Reduction in survey time But, unlikely to use it to cross a process plant Better than targets Targets are scanned at very high redundancy which calculates a reliable mean despite higher errors due to high incidence angle Uses all the geometrical information in the cloud This is only valid when that information has reliable errors attached to it.

29 In Summary: Automatic cloud registration in engineering measurement is: Probably possible with current tools Requires integration of tools and processes Not the be-all and end-all of scanning registration Not a replacement for proper error propagation

30 Geomatics Engineering at the University of Calgary 19 professors 50 undergraduate students per year 80 graduate students $4.5 research a year (Canadian) The University (of Calgary) has arguably the most well focused and dynamic department of Geomatics Engineering anywhere in the world." Stephen Booth, Editor, Surveying World