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Robotics Lab, Mines ParisTech. «CO-REGISTRATION OF HETEREGENEOUS GEOREFERENCING 3D DATA : CONTRIBUTION OF MOBILE POINT CLOUDS CORRECTION ». Dr. Taha Ridene. [email protected]. July 2010. Outlines. Introduction Rigid registration algorithms Results of registration Conclusion. - PowerPoint PPT Presentation
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«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
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
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
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
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
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
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
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
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
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
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
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
Correction using DSMICP-SAR-ICP
Introduction Rigid registration algorithms Results of registration Conclusion
HPS-ICP
Global resultsGlobal performance
Introduction Rigid registration algorithms Results of registration Conclusion
15/19
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
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
Introduction Rigid registration algorithms Results of registration Conclusion
Publications
17/19
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
T H A N K Y O U F O R Y O U R A T T E N T I O N
[email protected] 18/19
Global resultsGlobal performance
Introduction Rigid registration algorithms Results of registration Conclusion