taha.ridene@mines-paristech.fr

«CO-REGISTRATION OF HETEREGENEOUS GEOREFERENCING 3D DATA : CONTRIBUTION OF MOBILE POINT CLOUDS CORRECTION »

Robotics Lab, Mines ParisTech

July 2010

Dr. Taha RideneDr. Taha Ridene

I. Introduction

II. Rigid registration algorithms

III. Results of registration

IV. Conclusion

Outlines

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I. 3D data production

II. Exploitation of 3D

Video game Touristic Military

Acquisition

Data processing

Digitalizing of the territories and their resources and exploitation of multimedia information

17 partners : 7 companies and 10 public research labs

GPS

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Global context3D Data setsNeed for correction

Introduction Rigid registration algorithms Results of registration Conclusion

To obtain a 3D mapping database : textured, triangulated and geo-referenced

Approach: 3D heteregenous representation fusion

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I. 3D data production

Acquisition

Data processing

Global context3D Data setsNeed for correction

Introduction Rigid registration algorithms Results of registration Conclusion

Digitalizing of the territories and their resources and exploitation of multimedia information

17 partners : 7 companies and 10 public research labs

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Global context3D Data setsNeed for correction

Introduction Rigid registration algorithms Results of registration Conclusion

Mensi Trimble

Mines ParisTech IGN

3D heteregenous data geo referenced in Lambert2

(1St initialization of the registration)

Coherent datasets

Registration/correction

Data fusion layout

Fusion

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Global context3D Data setsNeed for correction

Introduction Rigid registration algorithms Results of registration Conclusion

3D point clouds

3D point clouds

DSMDSM

P1 P2 P3

T2T3

Portion of Interest

T1

P1ref

Reference portion

InputInput

12

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Correction using DSMICP-SAR-ICP

Introduction Rigid registration algorithms Results of registration Conclusion

HPS-ICP

TopConvergence

criteria

M Q

M’ Q’

Pre-processing (s)Pre-processing (s)

Itéra

tif s

tep

Itéra

tif s

tep

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Correction using DSMICP-SAR-ICP

Introduction Rigid registration algorithms Results of registration Conclusion

HPS-ICP

KD-Tree acceleration (Mount and Sunil., 2006)

Point to point Point to surface

Least mean square (LMS) (William et al., 1988)

Dynamic threshold

(Ridene and Goulette, 2008, CTL 2009, RFPT 2010)(Ridene and Goulette, 2008, CTL 2009, RFPT 2010)

Classical ICP-SA meet difficulty

Help the algorithm from the start

and fails

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Correction using DSMICP-SAR-ICP

Introduction Rigid registration algorithms Results of registration Conclusion

HPS-ICP

M Q

K.N.N computingK.N.N computing

ICP-SAICP-SA

Initi

alis

ation

by

RAN

SAC

Initi

alis

ation

by

RAN

SAC

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Correction using DSMICP-SAR-ICP

Introduction Rigid registration algorithms Results of registration Conclusion

HPS-ICP

RANSAC (RANdom Sample Concensus) (Fischler and Bolles,1981)

In registration(Chen et al., 1999; Bae and Lichti, 2008)

(Ridene and Goulette, CIRA 2009)(Ridene and Goulette, CIRA 2009)

M Q

M’ Q’

Horizontal plan extractionHorizontal plan extraction

Tz estimationTz estimation

Apply T-initApply T-init

2D projection2D projection

T2D estimationT2D estimation

ICP-SAICP-SA

Initi

aliz

ation

by

horiz

onta

l pla

n se

gmen

tatio

n an

d re

gist

ratio

nIn

itial

izati

on b

y ho

rizon

tal p

lan

segm

enta

tion

and

regi

stra

tion

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Correction using DSMICP-SAR-ICP

Introduction Rigid registration algorithms Results of registration Conclusion

HPS-ICP

(Jebbari et al, 2009)(Jebbari et al, 2009)

M Q

M’ Q’

Horizontal plan extractionHorizontal plan extraction

Tz estimationTz estimation

Apply T-initApply T-init

2D projection2D projection

T2D estimationT2D estimation

ICP-SAICP-SA

Initi

aliz

ation

by

horiz

onta

l pla

n se

gmen

tatio

n an

d re

gist

ratio

nIn

itial

izati

on b

y ho

rizon

tal p

lan

segm

enta

tion

and

regi

stra

tion

RANSAC Segmentation AlgorithmRANSAC Segmentation Algorithm

« Profile-Based » segmentation« Profile-Based » segmentation

3D

2D

DoG FilterDoG Filter

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Correction using DSMICP-SAR-ICP

Introduction Rigid registration algorithms Results of registration Conclusion

HPS-ICP

M Q

M’ Q’

Horizontal plan extractionHorizontal plan extraction

Tz estimationTz estimation

Apply T-initApply T-init

2D projection2D projection

T2D estimationT2D estimation

ICP-SAICP-SA

Initi

aliz

ation

by

horiz

onta

l pla

n se

gmen

tatio

n an

d re

gist

ratio

nIn

itial

izati

on b

y ho

rizon

tal p

lan

segm

enta

tion

and

regi

stra

tion

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Correction using DSMICP-SAR-ICP

Introduction Rigid registration algorithms Results of registration Conclusion

HPS-ICP

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Global resultsGlobal performance

Introduction Rigid registration algorithms Results of registration Conclusion

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Iteration CPU Time (s) Acceleration factor

Without KD-Tree

KD-tree

P2/DSM 33 620,8 18,08 34,2

P8/DSM 35 18,08 7,49 31,3

P1/P2ref 24 1139,52 34,8 32,74

Acceleration factor ~32Acceleration factor ~32

Intel(R) Xeon (R) CPU 5130 @ 2.00GHZ avec 2Go de RAM

Traited area: ~ 5 millions of point

Global time = 3 mnGlobal time = 3 mn

taha.ridene@mines-paristech.fr

Global resultsGlobal performance

Introduction Rigid registration algorithms Results of registration Conclusion

Rigid Registration : Possible solution for Mobile Mapping Systems

geo-referencing/localization problems

Shift problems Correspondence after registration

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Introduction Rigid registration algorithms Results of registration Conclusion

Publications

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Journals

1.T. Ridene and F. Goulette. Coregistration of DSM and 3D point clouds acquired by a mobile mapping system. Geodetic sciences bulletin - Special Issue on Mobile Mapping Technology, 15(5) :824-838, 2009d.2.T. Ridene and F. Goulette. Recalage de relevés laser fixes et mobiles sur MNS pour la cartographie numérique 3D. Revue Française de photogrammétrie et de télédétection, Jan. 2009a.

International conferences

1.T. Ridene and A. Manzanera. Mécanismes d’attention visuelle sur rétine artificielle. TAIMA’07., Hammamet, May. 2007.2.T. Ridene and F. Goulette. Recalage hétérogène de données 3D d’environnements urbains. MajecSTIC’08 (IEEE France), Oct. 2008.3.T. Ridene and F. Goulette. Recalage de relevés laser fixes et mobiles sur MNS pour cartographie numérique 3D. Colloque Techniques Laser Pour l’Etude des Environnements Naturels et Urbains, Jan. 2009.4.T. Ridene and F. Nashashibi. Localisation précise d’un système de cartographie mobile pour la numérisation 3D d’environnement Urbain. ATEC-ITS, Feb. 2009. 5.T. Ridene and F. Goulette. Registration together and to DSM of several 3D point clouds issued from a Mobile Mapping System. Mobile Mapping Technologies, jul. 2009b.6.T. Ridene and F. Goulette. Registration of fixed-and-mobile- based terrestrial laser data sets with DSM. pages 375-380, dec. 2009c. doi : 10.1109/CIRA.2009.5423176.7.T. Ridene and F. Goulette. Feature-based quality evaluation of 3D heterogeneous data registration. In Proceedings of SPIE, volume 7526, page 75260Z, 2010.8.T. Ridene and F. Goulette. USAGE DE LA CARTOGRAPHIE 3D POUR L’URBANISME ET LE SERVICE DE PROXIMITÉ -EXEMPLE D’APPLICATION AU DIAGNOSTIC D’ACCESSIBILITÉ. (Accepted) GEOTUNIS 2010

IJCV special ISSUE Sptember 2010

taha.ridene@mines-paristech.fr

T H A N K Y O U F O R Y O U R A T T E N T I O N

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Global resultsGlobal performance

Introduction Rigid registration algorithms Results of registration Conclusion