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Skyline-based registration of 3D laser scans

  • Andreas NüchterEmail author
  • Stanislav Gutev
  • Dorit Borrmann
  • Jan Elseberg
Article

Abstract

Acquisition and registration of terrestrial 3D laser scans is a fundamental task in mapping and modeling of cities in three dimensions. To automate this task marker-free registration methods are required. Based on the existence of skyline features, this paper proposes a novel method. The skyline features are extracted from panoramic 3D scans and encoded as strings enabling the use of string matching for merging the scans. Initial results of the proposed method in the old city center of Bremen are presented.

Keywords

LIDAR point cloud processing 3D city modeling marker-free registration place recognition 

CLC number

P208 

References

  1. [1]
    Booij O, Terwijn B Z, Krose B (2007) Navigation using an appearance based topological map[C]. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA’ 07), Rome, ItalyGoogle Scholar
  2. [2]
    Cummins M, Newman P (2007) Probabilistic appearance based navigation and loop closing[C]. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA’ 07), Rome, ItalyGoogle Scholar
  3. [3]
    Cummins M, Newman P (2008) Accelerated appearance only slam[C]. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA’ 08), Pasadena, CA, USAGoogle Scholar
  4. [4]
    Konolige K, Bowman J, Chen J D, et al.(2009) View-based maps[C]. Proceedings of the Robotics: Science and Systems (RSS’ 09), Seattle, USAGoogle Scholar
  5. [5]
    Valgren C, Lilienthal A J (2010) Sift, surf & seasons: Appearance-based long-term localization in outdoor environments[J]. Journal Robotics and Autonomous Systems (JRAS), 58(2):157–165CrossRefGoogle Scholar
  6. [6]
    Böhm J, Becker S (2007) Automatic marker-free registration of terrestrial laser scans using reflectance features[C]. Proceedings of 8th Conference on Optical 3D Measurement Techniques, Zürich, SwitzerlandGoogle Scholar
  7. [7]
    Wang Z, Brenner C (2008) Point based registration of terrestrial laser data using intensity and geometry features[C]. ISPRS Congress (’08), Beijing, ChinaGoogle Scholar
  8. [8]
    Kang Z, Li J, Zhang L, et al. (2009) Automatic registration of terrestrial laser scanning point clouds using panoramic reflectance images[J]. Sensors, (4): 2621–2646Google Scholar
  9. [9]
    Flint A, Dick A, van den Hengel A J (2007) Thrift: Local 3D structure recognition[C]. Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA’07), Glenelg, South AustraliaGoogle Scholar
  10. [10]
    Brenner C, Dold C, Ripperda N (2008) Coarse orientation of terrestrial laser scans in urban environments[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 63(1): 4–18CrossRefGoogle Scholar
  11. [11]
    Pathak K, Borrmann D, Elseberg J, et al. (2010) Evaluation of the robustness of planar patches based 3D-registration using marker-based ground-truth in an outdoor urban scenario[C]. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’ 10), Taipei, ChinaGoogle Scholar
  12. [12]
    Magnusson M, Andreasson H, Nüchter A, et al. (2009) Automatic appearance-based loop detection from 3D laser data using the normal distributions transform.[J]. Journal of Field Robotics (JFR), Special Issue on Three-Dimensional Mapping, 26(11–12):892–914Google Scholar
  13. [13]
    Huber D (2002) Automatic three-dimensional modeling from reality[D]. Pittsburgh: Carnegie Mellon UniversityGoogle Scholar
  14. [14]
    Steder B, Grisetti G, Burgard W (2010) Robust place recognition for 3D range data based on point features[C]. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA’ 10), Anchorage, AlaskaGoogle Scholar
  15. [15]
    Barnea S, Filin S (2008) Keypoint based autonomous registration of terrestrial laser point-clouds[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 63(1): 19–35CrossRefGoogle Scholar
  16. [16]
    Besl P, McKay N (1992) A method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 14(2): 239–256CrossRefGoogle Scholar
  17. [17]
    Ramadingam S, Bouaziz S, Sturm P, et al. (2009) SKYLINE2GPS: localization in urban canyons using Omni-Skylines[C]. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’ 10), Taipei, ChinaGoogle Scholar
  18. [18]
    Stein F, Medioni G (1995) Map-based localizatin using the panoramic horizon[J]. IEEE Transaction on Robotics and Automation, 6(11): 892–896CrossRefGoogle Scholar
  19. [19]
    Bazin J -C, Kweon C, Demonceaux C, et al. (2009) Dynamic programming and skyline extraction in catadioptric infrared images[C]. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA’ 09), Kobe, JapanGoogle Scholar
  20. [20]
    Goeman H, Clausen M (2002) A new practical linear space algorithm for the longest common subsequence problem[J]. Kybernetika, 38(1): 45–66Google Scholar
  21. [21]
    Fischler M A, Bolles R C (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 24(6): 381–395CrossRefGoogle Scholar

Copyright information

© Wuhan University and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Andreas Nüchter
    • 1
    Email author
  • Stanislav Gutev
    • 1
  • Dorit Borrmann
    • 1
  • Jan Elseberg
    • 1
  1. 1.Automation Group, School of Engineering and ScienceJacobs University Bremen gGmbHBremenGermany

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