A Real-time Algorithm for Acquiring Multi-Planar Volumetric Models with Mobile Robots

  • Sebastian Thrun
  • Wolfram Burgard
  • Deepayan Chakrabarti
  • Rosemary Emery
  • Yufeng Liu
  • Christian Martin
Conference paper
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 6)

Abstract

This paper summarizes recent research on developing autonomous robot systems than can acquire volumetric 3D maps with mobile robots in real-time. The core of our system is a real-time version of the popular expectation algorithm, developed for extracting scalar surfaces from sets of range scans (Martin, Thrun, 2002). Maps generated by this algorithm consists of a small number of planar rectangular surfaces, which are augmented by fine-grained polygons for non-flat environmental features. Experimental results obtained in a corridor-type environment illustrate that compact and accurate maps can be acquired in real-time from range and camera data.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Allen PK, Stamos I (2000) Integration of range and image sensing for photorealistic 3D modeling. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp 1435–1440.Google Scholar
  2. Bajcsy R, Kamberova G, Nocera L (2000) 3D reconstruction of environments for virtual reconstruction. In Proc. of the 4th IEEE Workshop on Applications of Computer Vision.Google Scholar
  3. Chatila R, Laumond JP (1985) Position referencing and consistent world modeling for mobile robots. In Proceedings of the 1985 IEEE International Conference on Robotics and Automation.Google Scholar
  4. Debevec PE, Taylor CJ, Malik J (1996) Modeling and rendering architecture from photographs. In Proc. of the 23rd International Conference on Computer Graphics and Interactive Techniques (SIGGRAPH).Google Scholar
  5. Dempster AP, Laird AN, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39(1):1–38.MATHMathSciNetGoogle Scholar
  6. Elfes A (1987) Sonar-based real-world mapping and navigation. IEEE Journal of Robotics and Automation, RA-3(3):249–265.CrossRefGoogle Scholar
  7. Gutmann JS, Konolige K (2000) Incremental mapping of large cyclic environments. In Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA).Google Scholar
  8. Hähnel D, Schulz D, Burgard W (2002) Map building with mobile robots in populated environments. In Proceedings of the Conference on Intelligent Robots and Systems (IROS), Lausanne, Switzerland.Google Scholar
  9. Iocchi L, Konolige K, Bajracharya M (2000) Visually realistic mapping of a planar environment with stereo. In Proceesings of the 2000 International Symposium on Experimental Robotics, Waikiki, Hawaii.Google Scholar
  10. Liu Y, Emery R, Chakrabarti D, Burgard W, Thrun S (2001) Using EM to learn 3D models with mobile robots. In Proceedings of the International Conference on Machine Learning (ICML).Google Scholar
  11. Lu F, Milios E (1997) Globally consistent range scan alignment for environment mapping. Autonomous Robots, 4:333–349.CrossRefGoogle Scholar
  12. Lu F, Milios E (1998) Robot pose estimation in unknown environments by matching 2D range scans. Journal of Intelligent and Robotic Systems, 18:249–275.CrossRefGoogle Scholar
  13. Martin C, Thrun S (2002) Online acquisition of compact volumetric maps with mobile robots. In IEEE International Conference on Robotics and Automation (ICRA), Washington, DC.Google Scholar
  14. Moravec HP, Martin MC (1994) Robot navigation by 3D spatial evidence grids. Mobile Robot Laboratory, Robotics Institute, Carnegie Mellon University.Google Scholar
  15. Shum H, Han M, Szeliski R (1998) Interactive construction of 3D models from panoramic mosaics. In Proc. of the International Conference on Computer Vision and Pattern Recognition (CVPR).Google Scholar
  16. Thorpe C, Durrant-Whyte H (2001) Field robots. In Proceedings of the 10th International Symposium of Robotics Research (ISRR’01), Lorne, Australia.Google Scholar
  17. Thrun S (2001) A probabilistic online mapping algorithm for teams of mobile robots. International Journal of Robotics Research, 20(5):335–363.CrossRefGoogle Scholar
  18. Thrun S (2002) Robotic mapping: A survey. In Lakemeyer G, Neberl B (eds) Exploring Artificial Intelligence in the New Millenium. Morgan Kaufmann. To appear.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Sebastian Thrun
    • 1
  • Wolfram Burgard
    • 1
  • Deepayan Chakrabarti
    • 1
  • Rosemary Emery
    • 1
  • Yufeng Liu
    • 1
  • Christian Martin
    • 1
  1. 1.Carnegie Mellon UniversityPittsburghUSA

Personalised recommendations