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ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective...

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ICL: ITERATIVE CLOSEST LINE ICL: ITERATIVE CLOSEST LINE A NOVEL POINT CLOUD REGISTRATION A NOVEL POINT CLOUD REGISTRATION ALGORITHM BASED ON LINEAR FEATURES ALGORITHM BASED ON LINEAR FEATURES Majd ALSHAWA 1/16
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Page 1: ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective State of the art Proposed method Test and experiment Conclusion 3/16 State of the art

ICL: ITERATIVE CLOSEST LINEICL: ITERATIVE CLOSEST LINE

A NOVEL POINT CLOUD REGISTRATION A NOVEL POINT CLOUD REGISTRATION ALGORITHM BASED ON LINEAR FEATURESALGORITHM BASED ON LINEAR FEATURES

Majd ALSHAWA

1/16

Page 2: ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective State of the art Proposed method Test and experiment Conclusion 3/16 State of the art

Presentation outline

• Objective

• State of the art

• Proposed method

• Test and experiment

• Conclusion and future work

2/16

Page 3: ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective State of the art Proposed method Test and experiment Conclusion 3/16 State of the art

What is 3D matching ?

• Multiple scans for the same object• Need to put them in the same coordinate frame

Objective • match an unknown coordinate point cloud « data or scene » with a known coordinate cloud « model »

• Pairing + Rigid transformation

Particular case

Georeferenced point cloud

Topographic

scannerReal-time coordinate

acquisition

Superposition verification

Objective State of the art Proposed method Test and experiment Conclusion

3/16

Page 4: ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective State of the art Proposed method Test and experiment Conclusion 3/16 State of the art

State of the art

• Spheres aiding registration

• Object-based registration

“château d’eau “ details

4/16

Objective State of the art Proposed method Test and experiment Conclusion

Page 5: ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective State of the art Proposed method Test and experiment Conclusion 3/16 State of the art

ICP Method : Iterative Closest Point

drawback:

High number of iterations to converge (unknown approximative coordinates)

2∑ −+= iqtiRpE

• Couple the nearest points• Establish the error function :

• Minimize this function and deduce rigid transformation components• Apply this transformation to the data point cloud

Iterate the following steps :Iterate the following steps :

5/16

Objective State of the art Proposed method Test and experiment Conclusion

Page 6: ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective State of the art Proposed method Test and experiment Conclusion 3/16 State of the art

Proposed method: ICL : Iterative Closest Line

Data acquisition

Noise removal

ICP method

Line extraction

ICL: Form ICP

ICL : alternative Form

• ICL : an evolution of ICP

• Lines match geometric primitives

6/16

Objective State of the art Proposed method Test and experience Conclusion

Page 7: ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective State of the art Proposed method Test and experiment Conclusion 3/16 State of the art

Where?

Points where a remarkable normal direction change occurs

Why lines ?

• High detection possibility from many scans (invariant features)

• High registration control (two lines are sufficient)

• Reutilisation possible in other applications

7/16

Objective State of the art Proposed method Test and experiment Conclusion

Page 8: ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective State of the art Proposed method Test and experiment Conclusion 3/16 State of the art

Line extractionIncremental method

• Point by point modelisation

• Adding the closest point each time

• Least square adjustement to fit a line

Advantages:

• Simple and precise

• Low number of user-provided parameters

Drawback :

Execution time

8/16

Objective State of the art Proposed method Test and experiment Conclusion

Page 9: ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective State of the art Proposed method Test and experiment Conclusion 3/16 State of the art

Line extractionRANSAC (Random Sample Consensus)

• Trace a line through two random points

• Measure all distances

• Point number – distance criteria

• Iterate the procedure until acceptable percentage of the cloud is modeled

Advantages:

• Simple fast methodDrawback

• Probabilism

9/16

Objective State of the art Proposed method Test and experiment Conclusion

Page 10: ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective State of the art Proposed method Test and experiment Conclusion 3/16 State of the art

Line extractionHough Transform

Principle: line-point duality in 2D space ( coordonnates-parameters)

( )mm θρ ,( )mm θρ ,( )mm θρ , θθρ sincos YX +=10/16

Objective State of the art Proposed method Test and experiment Conclusion

COORDINATE SPACE PARAMETER SPACE

Page 11: ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective State of the art Proposed method Test and experiment Conclusion 3/16 State of the art

Line extraction Hough Transform

• Representing the curves on a histogram

• Regional maximums search

• Inversing the transform to detect the lines

( )mm θρ ,( )mm θρ ,( )mm θρ , θθρ sincos YX +=

advantage:

• Fast method

Drawbacks:

• Too much threshold to provide

• Incapacity in some cases

11/16

Objective State of the art Proposed method Test and experiment Conclusion

Page 12: ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective State of the art Proposed method Test and experiment Conclusion 3/16 State of the art

ICL Algorithm : ICP form

Iterate the following steps :

∑=

−=pN

ipixi

p

RooN

TRf1

21),(

• Line pairing

• Error function establishement :

• Rotation matrix (R) calculation

• Rotate the « data » cloud

• Shift (T) calcuation

TaRa

TaRa

+=′

+=′

22

11

1a

2a

R

′1a

′2a

T

Non

-inea

r pr

oble

mLi

nar

prob

lem

12/16

Objective State of the art Proposed method Test and experiment Conclusion

Page 13: ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective State of the art Proposed method Test and experiment Conclusion 3/16 State of the art

Alternative form

⎟⎟⎟

⎜⎜⎜

−−−

+⎟⎟⎟

⎜⎜⎜

⎛=

⎟⎟⎟

⎜⎜⎜

⎛+

⎟⎟⎟

⎜⎜⎜

⎟⎟⎟

⎜⎜⎜

−−−

+⎟⎟⎟

⎜⎜⎜

⎛=

⎟⎟⎟

⎜⎜⎜

⎛+

⎟⎟⎟

⎜⎜⎜

AB

AB

AB

A

A

A

T

T

T

AB

AB

AB

A

A

A

T

T

T

ZZYYXX

ZYX

ZYX

RZYX

ZZYYXX

ZYX

ZYX

RZYX

2

3

2

2

1

1

1

1

λ

λ

B’

A’

1

2

B

A

1

2

Habib et al 03

Unavoidable equation linearisation

13/16

Objective State of the art Proposed method Test and experiment Conclusion

Page 14: ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective State of the art Proposed method Test and experiment Conclusion 3/16 State of the art

60 mm (distance), 0.5 degree (direction)

Coupling threshold

13Paired lines

3348Extracted lines

35 mm20 mmDistance threshold

2030Number threshold

154019527Potential point number

292706601952Point number

50 mm at 60 m

30 mm at 60 mlinear resolution

I13R14Station

School of Pontonniers

R14

Two different scans

I13

Line extraction taking into account the following parameters:

RMS : 0.3 cmRMS : 0.5 m Gon

0.20.30.211

0.2-0.80.3-5

0.20.20.312

σ (cm)Shift (cm)σ (m gon)rotation (m gon)

ICL : ICP form

RMS : 0.4 cmRMS 0.3 m Gon

0.20.60.29

0.3-1.50.2-4

0.31.80.22

σ (cm)Shift (cm)σ mGonRotation( m Gon)

méthode ICL forme alternative

ICL calculation:

14/16

Objective State of the art Proposed method Test and experiment Conclusion

Page 15: ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective State of the art Proposed method Test and experiment Conclusion 3/16 State of the art

Conclusion

Line extraction

•Lack of an overall method encompassing all cases

•High sensitivity to thresholds

•Extracting lines in two steps: a more efficient solution

Registration •High sensibility for the last step result

•The proposed method helps to evaluate the previous topographical operations

15/16

Objective State of the art Proposed method Test and experiment Conclusion

Page 16: ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective State of the art Proposed method Test and experiment Conclusion 3/16 State of the art

Perspectives•Line extraction as plans contours

•Using other geometric features during the registration

•Potential point extraction study

•Supplying approximative coordinates

Positives / negatives

•Highlighting the effect of the geometric complexity

•Accelerating the registration procedure

Drawbacks• Hindrance when no protruding or recessed details exist

•Redundancy decrease according to line detection low accuracy

Advantages

16/16

Objective State of the art Proposed method Test and experiment Conclusion

Page 17: ICL: ITERATIVE CLOSEST LINE/alisec/ISPRS_SS_2007/gradivo...Superposition verification Objective State of the art Proposed method Test and experiment Conclusion 3/16 State of the art

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