Scan-Point Planning and 3-D Map Building for a 3-D Laser Range Scanner in an Outdoor Environment

  • Keiji Nagatani
  • Takayuki Matsuzawa
  • Kazuya Yoshida
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 62)


During search missions in disaster environments, an important task for mobile robots is map building. An advantage of three-dimensional (3-D) mapping is that it can provide depictions of disaster environments that will support robotic teleoperations used in locating victims and aid rescue crews in strategizing. However, the 3-D scanning of an environment is timeconsuming because a 3-D scanning procedure itself takes a time and scan data must be matched at several locations. Therefore, in this paper, we propose a scan-point planning algorithm to obtain a large scale 3-D map, and we apply a scan-matching method to improve the accuracy of the map. We discuss the use of scan-point planning to maintain the resolution of sensor data and to minimize occlusion areas. The scan-matching method is based on a combination of the Iterative Closest Point (ICP) algorithm and the Normal Distribution Transform (NDT) algorithm.We performed several experiments to verify the validity of our approach.


Search and Rescue Scan points planning 


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  1. 1.
    Besl, P.J., McKay, N.D.: A method for registration of 3-d shapes. IEEE Tran. on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)CrossRefGoogle Scholar
  2. 2.
    Biber, P., Straber, W.: The normal distributions transform: A new approach to laser scan matching. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2743–2748 (2003)Google Scholar
  3. 3.
    Ayache, N.: Artificial Vision for Mobile Robots. MIT Press, Cambridge (1991)Google Scholar
  4. 4.
    Ikeuchi, K., Sato, Y.: Modeling from Reality. Kluwer Academic Publishers, Dordrecht (2001)zbMATHGoogle Scholar
  5. 5.
    Rourke, J.O.: Art Gallery Theorems and Algorithms. Oxford University Press, Oxford (1987)zbMATHGoogle Scholar
  6. 6.
    Klein, K., Sequeira, V.: View planning for the 3d modelling of real world scenes. In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (2000)Google Scholar
  7. 7.
    Yamauchi, B.: A frontier-based approach for autonomous exploration. In: IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 146–151 (1997)Google Scholar
  8. 8.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)zbMATHGoogle Scholar
  9. 9.
    Fong, E.H.L., Adams, W., Crabbe, F.L., Schultz, A.C.: Representing a 3-d environment with a 2 1/2-d map structure. In: IEEE/RSJ Conference on Intelligent Robots and Systems, pp. 2986–2991 (2003)Google Scholar
  10. 10.
    Triebel, R., Pfaff, P., Burgard, W.: Multi-level surface maps for outdoor terrain mapping and loop closing. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2276–2282 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Keiji Nagatani
    • 1
  • Takayuki Matsuzawa
    • 1
  • Kazuya Yoshida
    • 1
  1. 1.Tohoku University 

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