International Journal of Computer Vision

, Volume 60, Issue 1, pp 5–24 | Cite as

An Automated Method for Large-Scale, Ground-Based City Model Acquisition

  • Christian Früh
  • Avideh Zakhor
Article

Abstract

In this paper, we describe an automated method for fast, ground-based acquisition of large-scale 3D city models. Our experimental set up consists of a truck equipped with one camera and two fast, inexpensive 2D laser scanners, being driven on city streets under normal traffic conditions. One scanner is mounted vertically to capture building facades, and the other one is mounted horizontally. Successive horizontal scans are matched with each other in order to determine an estimate of the vehicle's motion, and relative motion estimates are concatenated to form an initial path. Assuming that features such as buildings are visible from both ground-based and airborne view, this initial path is globally corrected by Monte-Carlo Localization techniques. Specifically, the final global pose is obtained by utilizing an aerial photograph or a Digital Surface Model as a global map, to which the ground-based horizontal laser scans are matched. A fairly accurate, textured 3D cof the downtown Berkeley area has been acquired in a matter of minutes, limited only by traffic conditions during the data acquisition phase. Subsequent automated processing time to accurately localize the acquisition vehicle is 235 minutes for a 37 minutes or 10.2 km drive, i.e. 23 minutes per kilometer.

laser scanning navigation self-localization mobile robots 3D modeling Monte-Carlo localization 

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References

  1. Antone, M.E. and Teller, S. 2000. Automatic recovery of relative camera rotations for urban scenes. In Proc. IEEE Conf. on ComputerVision andPattern Recognition, Hilton Head Island, pp. 282–289.Google Scholar
  2. Besl, P. and McKay,N. 1992.Amethod for registration of 3-D shapes. Trans. PAMI, 14(2).Google Scholar
  3. Brenner, C., Haala, N., and Fritsch, D. 2001.Towards fully automated 3D city model generation. Workshop on Automatic Extraction of Man-Made Objects from Aerial and Space Images III.Google Scholar
  4. Burgard,W., Fox, D., Hennig, D., and Schmidt, T. 1996. Estimating the absolute position of a mobile robot using position probability grids, AAAI.Google Scholar
  5. Chan, R., Jepson,W., and Friedman, S. 1998. Urban simulation: An innovative tool for interactive planning and consensus building. In Proceedings of the 1998 American Planning Association National Conference, Boston, MA, pp. 43–50.Google Scholar
  6. Cox, I.J. 1991. Blanche-An experiment in guidance and navigation of an autonomous robot vehicle. IEEE Transactions on Robotics and Automation, 7:193–204.Google Scholar
  7. Debevec, P.E., Taylor, C.J., and Malik, J. 1996. Modeling and rendering architecture from photographs. In Proc. of ACM SIGGRAPH.Google Scholar
  8. Dellaert, F., Seitz, S., Thorpe, C., and Thrun, S. 2000. Structure from motion without correspondence. In IEEE Conference on Computer Vision and Pattern Recognition.Google Scholar
  9. Fox, D., Burgard,W., Thrun, S. 1999. Markov localization for mobile robots in dynamic environments.Journal of Artificial Intelligence Research, 11:391–427.Google Scholar
  10. Fox. D., Thrun, S., Dellaert, F., and Burgard,W. 2000. Particle filters for mobile robot localization. In Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas, and N. Gordon (Eds.), Springer Verlag, New York.Google Scholar
  11. Früh, C. and Zakhor, A. 2001. Fast 3D model generation in urban environments. In IEEE Conf. on Multisensor Fusion and Integration for Intelligent Systems, Baden-Baden, Germany, pp. 165–170.Google Scholar
  12. Früh, C. and Zakhor, A. 2001. 3D model generation of cities using aerial photographs and ground level laser scans. Computer Vision and Pattern Recognition, Hawaii, USA, vol. 2. 2, pp. II-31–8.Google Scholar
  13. Früh, C. and Zakhor, A. 2002. Data processing algorithms for generating textured 3D building facade meshes from laser scans and camera images. In Proc. Int'l Symposium on 3D Processing, Visualization and Transmission 2002, Padua, Italy, pp. 834–847.Google Scholar
  14. Früh, C. 2002. Automated 3D model generation for urban environments. PhD Thesis, University of Karlruhe, Germany.Google Scholar
  15. Frere, D., Vandekerckhove, J., Moons, T., and Van Gool, L. 1998. Automatic modelling and 3D reconstruction of urban buildings from aerial imagery. In IEEE International Geoscience and Remote Sensing Symposium Proceedings, Seattle, pp. 2593–2596.Google Scholar
  16. Gutmann, J.-S. and Konolige, K. 1999. "Incremental mapping of large cyclic environments. In International Symposium on Computational Intelligence in Robotics and Automation (CIRA'99), Monterey.Google Scholar
  17. Gutmann, J.-S. and Schlegel, C. 1996. Amos: Comparison of scan matching approaches for self-localization in indoor environments. In Proceedings of the 1st Euromicro Workshop on Advanced Mobile Robots.Google Scholar
  18. Hähnel, D., Burgard, W., and Thrun, S. 2001. Learning compact 3D models of indoor and outdoor environments with a mobile robot. 4th European Workshop on Advanced Mobile Robots (EUROBOT' 01).Google Scholar
  19. Huertas, A., Nevatia, R., and Landgrebe, D. 1999. Use of hyperspectral data with intensity images for automatic building modeling. In Proc. of the Second International Conference on Information Fusion, Sunnyvale, vol. 2, no. 2, pp. 680–687.Google Scholar
  20. Jensfelt, P. and Kristensen, S. 1999. Active global localization for a mobile robot using multiple hypothesis tracking. In Proceedings of the IJCAI Workshop on Reasoning with Uncertainty in Robot Navigation, pp. 13–22, Stockholm, Sweden, IJCAI.Google Scholar
  21. Kawasaki, H., Yatabe, T., Ikeuchi, K., and Sakauchi, M. 1999. Automatic modeling of a 3D city map from real-world video. In Proceedings ACM Multimedia, Orlando, USA, pp. 11–18.Google Scholar
  22. Koenig, S. and Simmons, R. 1998. A robot navigation architecture based on partially observable Markov decision process models.Google Scholar
  23. Koch, R., Pollefeys, M., and van Gool, L. 1999. Realistic 3D scene modeling from uncalibrated image sequences. ICIP'99, Kobe: Japan, pp. II 500–504.Google Scholar
  24. Konolige, K. and Chou, K. 1999. Markov localization using correlation. In Proc. International Joint Conference on Artificial Intelligence (IJCAI'99), Stockholm.Google Scholar
  25. Lu, F. and Milios, E. 1994. Robot pose estimation in unknown environments by matching 2D range scans. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google Scholar
  26. Lu, F. and Milios, E. 1997. Robot pose estimation in unknown environments by matching 2D range scans. Journal of Intelligent and Robotic Systems, 18.Google Scholar
  27. Lu, F. and Milios, E. 1997. Globally consistent range scan alignment for environment mapping.Autonomous Robots, 4:333–349.Google Scholar
  28. Maas, H.-G. 2001. The suitability of airborne laser scanner data for automatic 3D object reconstruction. Third Int'l Workshop on Automatic Extraction of Man-Made Objects, Ascona, Switzerland.Google Scholar
  29. Roumeliotis, S.I. and Bekey, G.A. 2000. Bayesian estimation and Kalman filtering: A unified framework for mobile robot localization. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), San Francisco, CA, pp. 2985–2992.Google Scholar
  30. Russell, S.J. and Norvig, P. 1995. Articial Intelligence: A modern approach. Chap. 17, Series in Articial Intelligence, Prentice Hall.Google Scholar
  31. Seitz, S. and Dyer, C. 1997. Photorealistic scene reconstruction by voxel coloring. In Proc. CVPR 1997, pp. 1067–1073.Google Scholar
  32. Simmons, R. and Koenig, S. 1995. Probabilistic robot navigation in partially observable environments. In Proc. of International Joint Conference on Artificial Intelligence, Montreal, vol. 2, pp. 1080-1087.Google Scholar
  33. Stamos, I. and Allen, P.E. 2000. 3-D model construction using range and image data. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition, Hilton Head Island, pp. 531–536.Google Scholar
  34. Szeliski, R. and Kang, S. 1995. Direct methods for visual scene reconstruction. IEEEWorkshop on Representation of Visual Scenes, pp. 26–33.Google Scholar
  35. Thrun, S. 2000. Probabilistic algorithms in robotics. AI Magazine, vol. 21, American Assoc. Artificial Intelligence, pp. 93–109.Google Scholar
  36. Thrun, S., Burgard, W., and Fox, D. 2000. A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping. In Proc. of ICRA 2000, San Francisco, vol. 1, no. 4, pp. 321–328.Google Scholar
  37. Thrun, S., Fox, D., Burgard,W., and Dellaert, F. 2001. Robust Monte Carlo localization for mobile robots. Artificial Intelligence Journal.Google Scholar
  38. Thrun, S. 2001. A probabilistic online mapping algorithm for teams of mobile robots. International Journal of Robotics Research, 20(5):335–363.Google Scholar
  39. Triggs, B., McLauchlan, P.F., Hartley, R.I., and Fitzgibbon, A.W. 2000.Bundle adjustment-A modern synthesis. In Proc. International Workshop on Vision Algorithms, Corfu, pp. 298–372.Google Scholar
  40. Weiss, G., Wetzler, C., and von Puttkamer, E. 1994. Keeping track of position and orientation of moving indoor systems by correlation of range-finder scans. In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Google Scholar
  41. Zhao, H. and Shibasaki, R. 1999. A system for reconstructing urban 3D objects using ground-based range and CCD images. In Proc. of International Workshop on Urban Multi-Media/3D Mapping, Tokyo.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Christian Früh
    • 1
  • Avideh Zakhor
    • 1
  1. 1.Video and Image Processing LaboratoryUniversity of CaliforniaBerkeley

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