markless registration for scans of free form objects
TRANSCRIPT
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MARKLESS REGISTRATION FOR SCANS OF FREE-FORM OBJECTS
Laboratory of photogrammetry of NTUA Artemis Valanis, PhD Student Charalambos Ioannidis, Professor
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Target: to initialize the ICP algorithmin order to register partial scans of uniform or free-form objects
Difficulty: no targets present no characteristic points identifiable in the area of overlap
Problem identification
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Motivation
Front view Side view
Initial state
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Front view Side view
MotivationResult of ICP - no prior processing
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Various approaches for automatic ICP initialization:
Bae & Lichti, 2004 Geometric primitives Gelfand, 2005 Feature points Hansen, 2006 Plane-matching Makadia, 2006 Extended Gaussian
Images Biswas, 2006 Isosurfaces
Related Literature
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Bae & Lichti, 2004 Geometric primitives Gelfand, 2005 Feature points Hansen, 2006 Plane-matching Makadia, 2006 Extended Gaussian Images Biswas, 2006 Isosurfaces
Example Objects
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Constrained acquisition process Properly adjusted methods that:◦Recover the relative transformation between two or more
partial scans◦Approximately align the point clouds◦Enable the initialization of ICP ◦Achieve the optimal alignment of partial scans without the
use of targets or the identification of conjugate points
Proposed approach
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Worked cases
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Worked cases
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HDS2500FOV 40ox40o spot size = 6mm position accuracy = ±6mm (50m range)
Equipment used
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Key Idea
Y
XZ
Y
X Z
Y
X
Zω
Acquisition scenario
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Key Idea
Y
X Z
Y
X Z
Y
X
Zω
Y
X
Zω
Acquisition scenario Acquired dataProposed approach
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Front view Side view
Initial state
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Front view
Result of ICP combined with the proposed method
Front view Side view
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Data imported:2 scans acquired either by rotating the scan head vertically (ω angle) or horizontally (φ angle)
Process:The space of the unknown parameter (ω or φ angle) is sequentially sampled in order to obtain an approximation of the unknown angle. If the value of the evaluated measure is minimized then an approximate value is derived
Proposed algorithm
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If the unknown rotation is ω
◦The ω is given an initial value 0 that is increased by 5g in every loop
◦For every ω value, a rotation matrix is calculated and applied to the point-cloud that needs to be registered
◦After the transformation, the area of overlap between the reference and the moving scan is calculated and a rectangular grid is defined
Sampling process 1/2
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◦The evaluated function i.e. the median of the distances of the two point clouds at the nodes of the grid along the Z direction, is derived based on 2D tesselations created for each point-cloud
◦Once the comparison measure reaches a minimum, the process is repeated at the respective interval with a step of 1g
◦When another minimum is detected, the final value is derived by a simple interpolation
Sampling process 2/2
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2 scans acquired by different ω angle 5 targets used to evaluate the results Algorithm implemented in Matlab Calculation of the unknown transform in Cyclone and
in Matlab
Method Validation
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Initial State
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Target distances as calculated for the original scans
T (X,Y,Z) 0.0000 0.0000 0.0000 (m)
R (ω,φ,κ) 0.0000 0.0000 0.0000 (grad)
TargetID X-error (m)
Y-error (m)
Z-error (m)
Total error (m)
4 -0.0202 1.9803 -0.5524 2.0560
7 -0.0251 2.4629 -0.4710 2.5077
3 -0.0254 2.5420 -0.4685 2.5849
1 -0.0322 3.3798 -0.8934 3.4960
13 -0.0321 3.1860 -0.8455 3.2964
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Results of the sampling process
Coarse sampling Fine sampling
ωι sign(mzi) |mzi| ωι sign(mzi) |mzi|
0 (-) 1.2349m 10 (-) 0.084m
5 (-) 0.6235m 11 (+) 0.0221m
10 (-) 0.084m Approximate value
15 (+) 0.4264m ωο =10.7920g
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Results after the approximate alignment
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Results after the approximate alignment
T (X,Y,Z): 0.0000 0.0000 0.0000 (m)
R (ω,φ,κ): 10.7920 0.0000 0.0000 (grad)
TargetID X-error (m)
Y-error (m)
Z-error (m)
Total error (m)
4 -0.0202 0.0070 0.0109 0.0240
7 -0.0251 0.0113 0.0090 0.0289
3 -0.0254 0.0106 0.0048 0.0279
1 -0.0322 0.0198 -0.0067 0.0384
13 -0.0321 0.0159 -0.0016 0.0359
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Result of ICP after the application of the proposed algorithm
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T(X,Y,Z): -0.0007 -0.0074 0.0122 (m)
R(ω,φ,κ): 10.8685 0.1134 0.0156 (grad)
TargetID X-error (m)
Y-error (m)
Z-error (m)
Total error (m)
4 0.0004 -0.0003 -0.0008 0.0010
7 0.0001 -0.0010 0.0012 0.0016
3 -0.0004 0.0013 0.0022 0.0026
1 -0.0013 0.0012 0.0015 0.0023
13 0.0005 0.0025 0.0007 0.0027
Results of ICP after the application of the proposed algorithm
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Application of the method for the monument of Zalongon
9 set-ups
14 scans in total
4 scans with no tagets
Back
3 set-ups
4 scans (2 single and a scan-pair)
Front
6 set-ups
10 scans (3 single, 2 scan-pairs and a scan-
triplet)
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Accuracy evaluation for 2 scan-pairs
Scan couple of set-up 5 (top to base)
ICP initialization for ωο =25.0327g
TargetID X-error (m) Y-error (m)
Z-error (m)
Total error (m)
11 -0.0003 -0.0020 0.0016 0.0026
10 -0.0003 -0.0038 0.0012 0.0040
Scan couple of set-up 6 (top to base)
ICP initialization for ωο =35.6083g
TargetID X-error (m) Y-error (m)
Z-error (m)
Total error (m)
16 -0.0016 -0.0071 0.0013 0.0074
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Scan triplet of set-up 7 (top to middle)
ICP initialization for ωο =18.1991g
Average alignment error = 0.0070m
Scan triplet of set-up 7 (top & middle to base)
ICP initialization for ωο =18.1828g
Average alignment error = 0.0067m
TargetIDX-error
(m) Y-error (m) Z-error (m)Total error
(m)
15 0.0005 -0.0027 0.0009 0.0029
6 0.0022 -0.0009 0.0009 0.0026
Accuracy evaluation for a scan-triplet
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Registration results
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3D surface model
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With minor modifications, it is as easily applied for horizontal rotations
Applicable also for sequences of scans acquired under the described conditions
Provides a solution in cases of serious space limitations
A non-elaborate and effective solution for all of those who have invested on similar equipment
Merits of the proposed approach
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Thank you for your attention!