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Robotics Lab, Mines ParisTech

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Presentation on theme: "Robotics Lab, Mines ParisTech"— Presentation transcript:

1 Robotics Lab, Mines ParisTech
«CO-REGISTRATION OF HETEREGENEOUS GEOREFERENCING 3D DATA : CONTRIBUTION OF MOBILE POINT CLOUDS CORRECTION » Dr. Taha Ridene July 2010

2 Outlines Introduction Rigid registration algorithms
Results of registration Conclusion 2/19

3 Rigid registration algorithms Results of registration
Global context 3D Data sets Need 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 I. 3D data production Acquisition Data processing II. Exploitation of 3D Video game Touristic Military GPS 3/19

4 Rigid registration algorithms Results of registration
Global context 3D Data sets Need 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 I. 3D data production Acquisition Data processing To obtain a 3D mapping database : textured, triangulated and geo-referenced Approach: 3D heteregenous representation fusion 4/19

5 Rigid registration algorithms Results of registration
Global context 3D Data sets Need for correction Introduction Rigid registration algorithms Results of registration Conclusion 1-5cm Interest building 10-20cm road 0.5-1m city Mines ParisTech IGN Mensi Trimble 5/19

6 Rigid registration algorithms Results of registration
Global context 3D Data sets Need for correction Introduction Rigid registration algorithms Results of registration Conclusion 3D heteregenous data geo referenced in Lambert2 (1St initialization of the registration) Registration/correction Fusion Coherent datasets Data fusion layout 6/19

7 Rigid registration algorithms Results of registration
Correction using DSM ICP-SA R-ICP Introduction Rigid registration algorithms Results of registration Conclusion HPS-ICP 3D point clouds DSM P1 P2 P3 Input 2 1 Portion of Interest T1 P1ref Reference portion T2 T3 7/19

8 K.N.N computing Reject outliers Tk estimation Apply Tk on M
Correction using DSM ICP-SA R-ICP Introduction Rigid registration algorithms Results of registration Conclusion HPS-ICP Dynamic threshold K.N.N computing Reject outliers Tk estimation Apply Tk on M Calculate error E(.) Top Convergence criteria false true M Q M’ Q’ Pre-processing (s) Itératif step Point to point Point to surface Least mean square (LMS) (William et al., 1988) KD-Tree acceleration (Mount and Sunil., 2006) (Ridene and Goulette, 2008, CTL 2009, RFPT 2010) 8/19

9 Rigid registration algorithms Results of registration
Correction using DSM ICP-SA R-ICP Introduction Rigid registration algorithms Results of registration Conclusion HPS-ICP and fails Classical ICP-SA meet difficulty Help the algorithm from the start 9/19

10 Rigid registration algorithms Results of registration
Correction using DSM ICP-SA R-ICP Introduction Rigid registration algorithms Results of registration Conclusion HPS-ICP Apply RANSAC for m sample Estimate Tm Apply Tm on M Th_ad = errorRansac . 2 M Q K.N.N computing ICP-SA Initialisation by RANSAC RANSAC (RANdom Sample Concensus) (Fischler and Bolles,1981) In registration (Chen et al., 1999; Bae and Lichti, 2008) (Ridene and Goulette, CIRA 2009) 10/19

11 Rigid registration algorithms Results of registration
Correction using DSM ICP-SA R-ICP Introduction Rigid registration algorithms Results of registration Conclusion HPS-ICP M Q M’ Q’ Horizontal plan extraction Tz estimation Apply T-init 2D projection T2D estimation ICP-SA Initialization by horizontal plan segmentation and registration (Jebbari et al, 2009) 11/19

12 Rigid registration algorithms Results of registration
Correction using DSM ICP-SA R-ICP Introduction Rigid registration algorithms Results of registration Conclusion HPS-ICP RANSAC Segmentation Algorithm « Profile-Based » segmentation M Q M’ Q’ Horizontal plan extraction Tz estimation Apply T-init 2D projection T2D estimation ICP-SA 3D 2D Initialization by horizontal plan segmentation and registration DoG Filter 12/19

13 Rigid registration algorithms Results of registration
Correction using DSM ICP-SA R-ICP Introduction Rigid registration algorithms Results of registration Conclusion HPS-ICP M Q M’ Q’ Horizontal plan extraction Tz estimation Apply T-init 2D projection T2D estimation ICP-SA Initialization by horizontal plan segmentation and registration 13/19

14 Rigid registration algorithms Results of registration
Global results Global performance Introduction Rigid registration algorithms Results of registration Conclusion 14/19

15 Rigid registration algorithms Results of registration
Global results Global performance Introduction Rigid registration algorithms Results of registration Conclusion Iteration CPU Time (s) Acceleration factor Without KD-Tree KD-tree P2/DSM 33 620,8 18,08 34,2 P8/DSM 35 7,49 31,3 P1/P2ref 24 1139,52 34,8 32,74 Acceleration factor ~32 Intel(R) Xeon (R) CPU 2.00GHZ avec 2Go de RAM Traited area: ~ 5 millions of point Global time = 3 mn 15/19

16 Rigid registration algorithms Results of registration
Introduction Rigid registration algorithms Results of registration Conclusion Rigid Registration : Possible solution for Mobile Mapping Systems geo-referencing/localization problems Correspondence after registration Shift problems 16/19

17 Publications IJCV special ISSUE Sptember 2010
Journals 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) : , 2009d. 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 T. Ridene and A. Manzanera. Mécanismes d’attention visuelle sur rétine artificielle. TAIMA’07., Hammamet, May T. Ridene and F. Goulette. Recalage hétérogène de données 3D d’environnements urbains. MajecSTIC’08 (IEEE France), Oct 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 T. Ridene and F. Nashashibi. Localisation précise d’un système de cartographie mobile pour la numérisation 3D d’environnement Urbain. ATEC-ITS, Feb 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. T. Ridene and F. Goulette. Registration of fixed-and-mobile- based terrestrial laser data sets with DSM. pages , dec. 2009c. doi : /CIRA T. Ridene and F. Goulette. Feature-based quality evaluation of 3D heterogeneous data registration. In Proceedings of SPIE, volume 7526, page 75260Z, 2010. 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 17/19

18 T H A N K Y O U F O R Y O U R A T T E N T I O N
18/19

19 Rigid registration algorithms Results of registration
Global results Global performance Introduction Rigid registration algorithms Results of registration Conclusion 14/19


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