The Climbing Sensor: 3D Modeling of Narrow Areas by Using Space- Time Analysis

  • Shintaro Ono
  • Ken Matsui
  • Katsushi Ikeuchi

In this chapter, we introduce a novel type of 3D scanning system, named ‘Climbing Sensor’. This system has been designed especially for scanning narrow areas, which are hard or inconvenient to scan by a conventional, commercial scanning system due to its radial laser emission, dimension, and limitation of field angle in some cases.

Our system equips a moving platform with two 1D range sensors (main and sub units) on a ladder-style electromotive lift, and it scans the whole target while it moves downwards and upwards along the ladder. The main unit is used for scanning the target, which repeats scanning in a perpendicular direction to the moving direction of the platform. The sub unit is used for localizing the platform, and it repeats the scanning process in a parallel direction. By using the spatiotemporal range scans acquired from the sub unit, we can accurately estimate the motion of the moving platform, and a correct 3D model can be constructed from data scanned by the main unit.

We applied this system to the Bayon Temple in Angkor Thom, Cambodia. The scanning results proved that the system gives an accurate 3D model, and that the system and the speed of the estimating process are effective.


Range Image Range Sensor Main Unit Range Point Scanning Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    Katsushi Ikeuchi, Kazuhide Hasegawa, Atsushi Nakazawa, Jun Takamatsu, Takeshi Oishi, Tomohito Masuda, “Bayon digital archival project,” Proc. International Conference on Virtual Systems and Multimedia (VSMM), Nov. 2004.Google Scholar
  2. [2]
    Yuichiro Hirota, “Designing a Laser Range Finder which is Suspended beneath a Balloon,” Proc. The 6th Asian Conference on Computer Vision (ACCV), Jan. 2004.Google Scholar
  3. [3]
    Tomohito Masuda, Yuichiro Hirota, Katsushi Ikeuchi, Ko Nishino, “Simultaneous Determination of Registration and Deformation Parameters among 3D Range Images,” Proc. 5th International Conference on 3D Digital Imaging and Modeling (3DIM), pp. 369-376, Jun. 2005.Google Scholar
  4. [4]
    Atsuhiko Banno, Katsushi Ikeuchi, “Shape Recovery of 3D Data Obtained from a Moving Range Sensor by Using Image Sequences,” Proc. 10th IEEE International Conference on Computer Vision (ICCV), pp. 792-799, Oct. 2005.Google Scholar
  5. [5]
    Atsuhiko Banno, Kazuhide Hasegawa, Katsushi Ikeuchi, “Motion Estimation of a Moving Range Sensor by Image Sequences and Distorted Range Data,” Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Aug. 2005.Google Scholar
  6. [6]
    Robert C. Bolles, H. Harlyn Baker, David H. Marimont, “Epipolar-plane image analysis: an approach to determining structure from motion,” International Journal on Computer Vision (IJCV), Vol. 1, No. 1, pp. 7-55, 1987.CrossRefGoogle Scholar
  7. [7]
    H. Harlyn Baker, Robert C. Bolles, “Generalizing epipolar plane image analysis on the spatio-temporal surface,” International Journal on Computer Vision (IJCV), Vol. 3, No. 1, pp. 33-49, 1989.CrossRefGoogle Scholar
  8. [8]
    Takeshi Oishi, Katsushi Ikeuchi, Atsushi Nakazawa, Ryo Kurazume, “Fast Simultaneous Alignment of Multiple Range Images using Index Images,” The 5th International Conference on 3D Digital Imaging and Modeling (3DIM), pp. 476-483, Jun. 2005.Google Scholar
  9. [9]
    Takeshi Oishi, Ryusuke Sagawa, Atsushi Nakazawa, Ryo Kurazume, Katsushi Ikeuchi, “Parallel Simultaneous Alignment of a Large Number of Range Images on Distributed Memory System,” IPSJ Transactions on Computer Vision and Image Media, Vol. 46, No. 9, pp. 2369-2378, Sep. 2005.Google Scholar
  10. [10]
    Shintaro Ono, Katsushi Ikeuchi, “Self-Position Estimation for Virtual 3D City Model Construction with the Use of Horizontal Line Laser Scanning,” International Journal of ITS Research (ITSJ), Vol. 2, No. 1, pp. 67-75, Oct. 2004.Google Scholar
  11. [11]
    Christian Früh, and Avideh Zakhor, “3D Model Generation for Cities Using Aerial Photographs and Ground Level Laser Scans”, Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 2, pp. 31-38, Dec. 2001.Google Scholar
  12. [12]
    Christian Früh and Avideh Zakhor. “Constructing 3D city models by merging ground-based and airborne views,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 2, pp. 562-569, Jun. 2003.Google Scholar
  13. [13]
    Huijing Zhao and Ryosuke Shibaski, “Reconstructing Urban 3D Model using Vehicle-Borne Laser Range Scanners,” Proc. International Conference on 3D Digital Imaging and Modeling (3DIM), pp. 349-356, May 2001.Google Scholar
  14. [14]
    Cyrax 2500, Leica Geosystems, Switzerland.
  15. [15]
    IMAGER 5003, Zoller+Fröhlich, Germany.
  16. [16]
    Nobitec Lift NP-4200, KSS Corporation, Japan.
  17. [17]
    LMS200, SICK AG, Germany.

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Shintaro Ono
  • Ken Matsui
  • Katsushi Ikeuchi
    • 1
  1. 1.Institute of Industrial ScienceThe University of TokyoMeguro-kuJapan

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