Extracting Structure of Buildings Using Layout Reconstruction

  • Matteo LupertoEmail author
  • Francesco Amigoni
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)


Metric maps, like occupancy grids, are the most common way to represent indoor environments in mobile robotics. Although accurate for navigation and localization, metric maps contain little knowledge about the structure of the buildings they represent. However, if explicitly identified and represented, this knowledge can be exploited in several tasks, such as semantic mapping, place categorization, path planning, human robot communication, and task allocation. The layout of a building is an abstract geometrical representation that models walls as line segments and rooms as polygons. In this paper, we propose a method to reconstruct two-dimensional layouts of buildings starting from the corresponding metric maps. In this way, our method is able to find regularities within a building, abstracting from the possibly noisy information of the metric map. Experimental results show that our approach performs effectively and robustly on different types of input metric maps, characterized by noise, clutter, and partial data.


  1. 1.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. The MIT Press, Cambridge (2005)zbMATHGoogle Scholar
  2. 2.
    Bormann, R., Jordan, F., Li, W., Hampp, J., Hägele, M.: Room segmentation: Survey, implementation, and analysis. In: Proceedings of ICRA, pp. 1019–1026 (2016)Google Scholar
  3. 3.
    Quattrini Li, A., Cipolleschi, R., Giusto, M., Amigoni, F.: A semantically-informed multirobot system for exploration of relevant areas in search and rescue settings. Auton. Robot. 40(4), 581–597 (2016)CrossRefGoogle Scholar
  4. 4.
    Liu, Z., von Wichert, G.: A generalizable knowledge framework for semantic indoor mapping based on Markov logic networks and data driven MCMC. Futur. Gener. Comput. Syst. 36, 42–56 (2014)CrossRefGoogle Scholar
  5. 5.
    Armeni, I., Sener, O., Zamir, A., Jiang, H., Brilakis, I., Fischer, M., Savarese, S.: 3D semantic parsing of large-scale indoor spaces. In: Proceedings of CVPR, pp. 1534–1543 (2016)Google Scholar
  6. 6.
    Mura, C., Mattausch, O., Villanueva, A.J., Gobbetti, E., Pajarola, R.: Automatic room detection and reconstruction in cluttered indoor environments with complex room layouts. Comput. Graph. 44, 20–32 (2014)CrossRefGoogle Scholar
  7. 7.
    Thrun, S.: Learning metric-topological maps for indoor mobile robot navigation. Artif. Intell. 99(1), 21–71 (1998)CrossRefGoogle Scholar
  8. 8.
    Brunskill, E., Kollar, T., Roy, N.: Topological mapping using spectral clustering and classification. In: Proceedings of IROS, pp. 3491–3496 (2007)Google Scholar
  9. 9.
    Mozos, O.: Semantic Labeling of Places with Mobile Robots. Springer Tracts in Advanced Robotics, vol. 61. Springer (2010)Google Scholar
  10. 10.
    Friedman, S., Pasula, H., Fox, D.: Voronoi random fields: Extracting the topological structure of indoor environments via place labeling. In: Proceedings of IJCAI, pp. 2109–2114 (2007)Google Scholar
  11. 11.
    Sjoo, K.: Semantic map segmentation using function-based energy maximization. In: Proceedings of ICRA, pp. 4066–4073 (2012)Google Scholar
  12. 12.
    Buschka, P., Saffiotti, A.: A virtual sensor for room detection. In: Proceedings of IROS, pp. 637–642 (2002)Google Scholar
  13. 13.
    Capobianco, R., Gemignani, G., Bloisi, D., Nardi, D., Iocchi, L.: Automatic extraction of structural representations of environments. In: Proceedings of IAS-13, pp. 721–733 (2014)Google Scholar
  14. 14.
    Oesau, S., Lafarge, F., Alliez, P.: Indoor scene reconstruction using feature sensitive primitive extraction and graph-cut. ISPRS J. Photogramm. 90, 68–82 (2014)CrossRefGoogle Scholar
  15. 15.
    Ochmann, S., Vock, R., Wessel, R., Klein, R.: Automatic reconstruction of parametric building models from indoor point clouds. Comput. Graph. 54, 94–103 (2016)CrossRefGoogle Scholar
  16. 16.
    Ambruş, R., Claici, S., Wendt, A.: Automatic room segmentation from unstructured 3-D data of indoor environments. IEEE Robot. Autom. Lett. 2(2), 749–756 (2017)CrossRefGoogle Scholar
  17. 17.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)CrossRefGoogle Scholar
  18. 18.
    Kiryati, N., Eldar, Y., Bruckstein, A.M.: A probabilistic hough transform. Pattern Recogn. 24(4), 303–316 (1991)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Suzuki, S., Abe, K.: Topological structural analysis of digitized binary images by border following. Comput. Vision Graph. 30(1), 32–46 (1985)CrossRefGoogle Scholar
  20. 20.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)CrossRefGoogle Scholar
  21. 21.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of KDD, pp. 226–231 (1996)Google Scholar
  22. 22.
    Yamauchi, B.: A frontier-based approach for autonomous exploration. In: Proceedings of CIRA, pp. 146–151 (1997)Google Scholar
  23. 23.
    Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with Rao-Blackwellized particle filters. IEEE Trans. Robot. 23, 34–46 (2007)CrossRefGoogle Scholar
  24. 24.
    Winterhalter, W., Fleckenstein, F., Steder, B., Spinello, L., Burgard, W.: Accurate indoor localization for RGB-D smartphones and tablets given 2D floor plans. In: Proceedings of IROS, pp. 3138–3143 (2015)Google Scholar
  25. 25.
    Behzadian, B., Agarwal, P., Burgard, W., Tipaldi, G.D.: Monte Carlo localization in hand-drawn maps. In: Proceedings of IROS, pp. 4291–4296 (2015)Google Scholar
  26. 26.
    Boniardi, F., Behzadian, B., Burgard, W., Tipaldi, G.D.: Robot navigation in hand-drawn sketched maps. In: Proceedings of ECMR, pp. 1–6 (2015)Google Scholar
  27. 27.
    Howard, A., Roy, N.: The robotics data set repository (Radish) (2003).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Politecnico di MilanoMilanItaly

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