Skip to main content

Advertisement

SpringerLink
Log in

Spatial Modelling for Mobile Robot’s Vision-based Navigation

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

We present an algorithm to model 3D workspace and to understand test scene for mobile robot’s navigation or human computer interaction. This has done by line-based modeling and recognition algorithm. Line-based recognition using 3D lines has been tried by many researchers however its reliability still needs improvement due to ambiguity of 3D line feature information from original images. To improve the outcome, we approach firstly to find real planes using given 3D lines and then to implement recognition process. The methods we use are principle component analysis (PCA), plane sweep, occlusion query, and iterative closest point (ICP). During the implementation, we also use 3D map information for localization. We apply this algorithm to real test scene images and find out our result can be useful to identify doors or walls in indoor environment with better efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Schmid, C., Zisserman, A.: Automatic line matching across views. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 666–671 (1997)

  2. Baillard, C., Zisserman, A., et al.: Automatic line matching and 3D reconstruction of buildings from multiple views. In: Conference on Automatic Extraction of GIS Objects from Digital Imagimary, Part 3-2W5, vol. 32, pp. 69–80 (2000)

  3. Bignone, F., et al.: Automatic extraction of generic house roofs from high resolution aerial imagery. In: European Conference on Computer Vision, pp. 85–96 (1996)

  4. Moons, T., et al.: Automatic modeling and 3D reconstruction of urban house roofs from high resolution aerial imagery. In: European Conference on Computer Vision, pp. 410–425 (1998)

  5. Lee, S.C., Nevatia, R.: Interactive 3D building modeling using a hierarchical representation. In: IEEE Workshop on Higher-Level Knowledge in 3D Modeling and Motion in conjuction with International Conference on Computer Vision. Nice (2003)

  6. Lee, S.C., Jung, S.K., Nevatia, R.: Automatic pose estimation of complex 3D building models. In: Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision (2002)

  7. Criminisi, A., Reid, I., Zisserman, A.: A plane measuring device. In: Image Processing on British Machine Vision Conference (1997)

  8. Baillard, C., Zisserman, A.: A plane-sweep strategy for the 3D reconstruction of buildings from multiple images. In: Proc. 19th. ISPRS Congress and Exhibition (2000)

  9. Zhang, Z., Faugeras, O.: Determining motion from 3D line segment matches: a comparative study. Image Vis. Comput. 9(1) 10–19 (1991)

    Article  Google Scholar 

  10. Chen, Y., Medioni, G.: Object modeling by registration of multiple range images. In: IEEE International Conference on Robotics and Automation, pp. 2724–2729 (1991)

  11. Besl, P., MaKay, N.: A method for registration of 3-D shapes. IEEE Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

    Article  Google Scholar 

  12. Kamgar-Parsi, B., Kamgar-Parsi, B.: Algorithms for matching 3D line sets. IEEE Pattern Anal. Mach. Intell. 26(5), 582–593 (2004)

    Article  Google Scholar 

  13. Guerra, C., Pascucci, V.: Line-based object recognition using Hausdorff distance: from range images to molecular secondary structures. Image Vis. Comput. 23(4), 405–415 (2005)

    Article  Google Scholar 

  14. Lee, S., Lee, S., Lee, J., Moon, D., Kim, E., Seo, J.: Robust recognition and pose estimation of 3D objects based on evidence fusion in a sequence of images. In: IEEE International Conference on Robotics and Automation (2007)

  15. Boissonnat, J.D., Snoeyink, J.: Efficient algorithms for line and curve segment intersection using restricted predicates. In: 15th Annual ACM Symposium of Computational Geometry, pp. 370–379 (1999)

  16. Balaban, I.: An optimal algorithm for finding segment intersections. In: Proceedings, 11th Annual ACM Symposium of Computational Geometry, pp. 211–219 (1995)

  17. Bartuschka, U., Mehlhorn, K., Naher, S.: A robust and efficient implementation of a sweep line algorithm for the straight line segment intersection problem. In: Proceedings, Workshop on Algorithm Engineering, pp. 124–135 (1997)

  18. Bentley, J.L., Ottoman, T.A.: Algorithm for reporting and counting geometric intersections. IEEE Trans. Comput. C-28(9), 643–647 (1979)

    Article  Google Scholar 

  19. Boissonnat, J.D., Preparata, F.P.: Robust plane sweep for intersecting segments. SIAM J. Comput. 29(5), 1401–1421 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  20. Palazzi, L., Snoeyink, J.: Counting and reporting red/blue segment intersections. CVGIP, Graph. Models Image Process. 56(4), 304–311 (1994)

    Article  Google Scholar 

  21. Cohen-Or, S., Chrysanthou, Y., Silva, C.T., Durand, F.: A survey of visibility for walkthrough applications. IEEE Trans. Vis. Comput. Graph. 9(3), 412–432 (2003)

    Article  Google Scholar 

  22. Ben-Arie, J.: The probabilistic peaking effect of viewed angles and distances with application to 3-D object recognition. IEEE Trans. Pattern Anal. Mach. Intell. 12(8), 760–774 (1990)

    Article  Google Scholar 

  23. Stein, F., Medioni, G.: Structural indexing: efficient 3D object recognition. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 125–145 (1992)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Korea Internet and Security Agency, Seoul, South Korea

    Dong-Young Yoo

  2. Korea University, Seoul, Republic of Korea

    Jin Young Choi

  3. Sungkyunkwan University, Suwon, South Korea

    Jae-Kyu Lee & Jin Wook Chung

  4. Sungkyunkwan University, Seoul, South Korea

    Seongjin Ahn

Authors
  1. Dong-Young Yoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jin Young Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jae-Kyu Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Seongjin Ahn
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jin Wook Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jae-Kyu Lee.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yoo, DY., Choi, J.Y., Lee, JK. et al. Spatial Modelling for Mobile Robot’s Vision-based Navigation. J Intell Robot Syst 63, 131–147 (2011). https://doi.org/10.1007/s10846-010-9500-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-010-9500-1

Keywords

Search

Navigation