Skip to main content
SpringerLink
Log in

A novel plane extraction approach using supervised learning

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

This paper presents a novel approach for the classification of planar surfaces in an unorganized point clouds. A feature-based planner surface detection method is proposed which classifies a point cloud data into planar and non-planar points by learning a classification model from an example set of planes. The algorithm performs segmentation of the scene by applying a graph partitioning approach with improved representation of association among graph nodes. The planarity estimation of the points in a scene segment is then achieved by classifying input points as planar points which satisfy planarity constraint imposed by the learned model. The resultant planes have potential application in solving simultaneous localization and mapping problem for navigation of an unmanned-air vehicle. The proposed method is validated on real and synthetic scenes. The real data consist of five datasets recorded by capturing three-dimensional(3D) point clouds when a RGBD camera is moved in five different indoor scenes. A set of synthetic 3D scenes are constructed containing planar and non-planar structures. The synthetic data are contaminated with Gaussian and random structure noise. The results of the empirical evaluation on both the real and the simulated data suggest that the method provides a generalized solution for plane detection even in the presence of the noise and non-planar objects in the scene. Furthermore, a comparative study has been performed between multiple plane extraction methods.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Jeong, W.Y., Lee, K.M.: Visual SLAM with line and corner features. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2570–2575 (2006)

  2. Cherian, A., Morellas, V., Papanikolopoulos, N.: Accurate 3D ground plane estimation from a single image. In: ICRA’09 Proceedings of the 2009 IEEE International Conference on Robotics and Automation, pp. 2243–2249 (2009)

  3. Jamal, A., Mishra, P., Rakshit, S., Singh, A.K., Kumar, M.: Real-time ground plane segmentation and obstacle detection for mobile robot navigation. In: International Conference on Emerging Trends in Robotics and Communication Technologies, pp. 314–317. IEEE Computer Society, Chennai (2010)

  4. Pears, N., Liang, B.: Ground plane segmentation for mobile robot visual navigation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 1513–1518, Hawaii, USA (2001)

  5. Viejo, D., Cazorla, M.: 3D plane-based egomotion for SLAM on semi-structured environment. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2761–2766, California, USA (2007)

  6. Ulas, C., Temeltas, H.: Plane-feature based 3D outdoor SLAM with Gaussian filters. In: International Conference on Vehicular Electronics and Safety (ICVES), pp. 13–18, Istanbul, Turkey (2012)

  7. Pathak, K., Birk, A., Vaškevičius, N., Poppinga, J.: Fast registration based on noisy planes with unknown correspondences for 3-D mapping. IEEE Trans. Robot. 26, 424–441 (2010)

    Google Scholar 

  8. Udrea, R.M., Vizireanu, N.: Iterative generalization of morphological skeleton. J. Electron. Imaging 16, 010501 (2007)

    Article  Google Scholar 

  9. Vizireanu, D.N., Pirnog, C., Lãzãrescu, V., Vizireanu, A.: The skeleton structure–an improved compression algorithm with perfect reconstruction. J. Digital Imaging 14, 241–242 (2001)

    Google Scholar 

  10. Vizireanu, N., Udrea, R.: Visual-oriented morphological foreground content grayscale frames interpolation method. J. Electron. Imaging 18, 020502 (2009)

    Article  Google Scholar 

  11. Prieto-Marañón, V., Cabrera-Gámez, J., Domínguez-Brito, A.C., Hernández-Sosa, D., Isern-González, J., Fernández-Perdomo, E.: Efficient plane detection in multilevel surface maps. J. Phys. Agents 5, 15–23 (2011)

    Google Scholar 

  12. Julkaisuja, A., Heikki, K., Jarvinen, M., Ukkola, A.: Randomized Hough transform. In: International Conference on Pattern Recognition, pp. 631–635. IEEE (1990)

  13. Kälviäinen, H., Hirvonen, P., Xu, L., Oja, E.: Probabilistic and non-probabilistic Hough transforms: overview and comparisons. Image Vis. Comput. 13, 239–252 (1995)

    Google Scholar 

  14. Kiryati, N., Eldar, Y., Bruckstein, A.M.: A probabilistic Hough transform. Pattern Recognit. 24, 303–316 (1991)

    Article  MathSciNet  Google Scholar 

  15. Okada, K., Kagami, S., Inaba, M., Inoue, H.: Plane segment finder: algorithm, implementation and applications. In: IEEE International Conference on Robotics and Automation (ICRA), vol. 2, pp. 2120–2125 (2001)

  16. Tarsha-Kurdi, F., Landes, T., Grussenmeyer, P.: Hough-transform and extended RANSAC algorithms for automatic detection of 3d building roof planes from lidar data. Int. Arch. Photogrammetry Remote Sens. Spatial Inf. Syst. 36, 407–412 (2007)

    Google Scholar 

  17. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 888–905 (2000)

    Article  Google Scholar 

  18. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–95 (1981)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computing, Blekinge Institute of Technology, 37179 , Karlskrona, Sweden

    J. Rafid Siddiqui, Mohammad Havaei & Siamak Khatibi

  2. Intelligent Sensing and Systems, Common-wealth Scientific and Research Organization (CSIRO), Hobart, Australia

    Craig A. Lindley

Authors
  1. J. Rafid Siddiqui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Havaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Siamak Khatibi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Craig A. Lindley
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. Rafid Siddiqui.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Siddiqui, J.R., Havaei, M., Khatibi, S. et al. A novel plane extraction approach using supervised learning. Machine Vision and Applications 24, 1229–1237 (2013). https://doi.org/10.1007/s00138-013-0482-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-013-0482-4

Keywords

Search

Navigation