Intelligent Service Robotics

, Volume 11, Issue 1, pp 1–24 | Cite as

A practical 2D/3D SLAM using directional patterns of an indoor structure

  • Keonyong Lee
  • Soo-Hyun Ryu
  • Changjoo Nam
  • Nakju Lett DohEmail author
Original Research Paper


This paper presents a practical two-dimensional (2D)/three-dimensional (3D) simultaneous localization and mapping (SLAM) algorithm using directional features for ordinary indoor environments; this algorithm is adaptable to various conditions, computationally inexpensive, and accurate enough to use for practical applications. The proposed algorithm uses odometry acquired from other sensors or other algorithms as the initial estimate and the directional features of indoor structures as landmarks. The directional features can only correct the rotation error of the odometry. However, we show that the greater part of the translation error of the odometry can also be corrected when the directional features are detected at almost positions accurately. In that case, there is no need to use other kinds of features to correct translation error. The directions of indoor structures have two advantages as landmarks. First, the extraction of them is not affected by obstacles. Second, the number of them is small regardless of the size of the building. Because of these advantages, the proposed SLAM algorithm shows robustness for parameters and lightweight properties. From extensive experiments with 2D/3D datasets taken from different buildings, we show the practicality of the proposed algorithm. We also demonstrate that the 2D algorithm runs in real time on a low-end smartphone.


Directional feature Indoor environments Kalman filters Lightweight algorithm Practical algorithm Simultaneous localization and mapping (SLAM) 



This paper is supported by three Korean government funds of 10073166, 2012M3A6A3055700, and 2014K1A3A1A19067398.

Supplementary material

11370_2017_234_MOESM1_ESM.mp4 (139.3 mb)
Supplementary material 1 (mp4 142653 KB)


  1. 1.
    Almansa A, Desolneux A, Vamech S (2003) Vanishing point detection without any a priori information. IEEE Trans Pattern Anal Mach Intell 25(4):502–507CrossRefGoogle Scholar
  2. 2.
    Wildenauer H, Vincze M (2007) Vanishing point detection in complex man-made worlds. In: International conference on image analysis and processing, pp 615–622Google Scholar
  3. 3.
    Tardif JP (2009) Non-iterative approach for fast and accurate vanishing point detection. In: IEEE international conference on computer vision, pp 1250–1257Google Scholar
  4. 4.
    Nguyen V, Harati A, Martinelli A, Siegwart R (2006) Orthogonal SLAM: a step toward lightweight indoor autonomous navigation. In: IEEE/RSJ international conference on intelligent robots and systems, pp 5007–5012Google Scholar
  5. 5.
    Lee YH, Nam C, Lee KY, Li YS, Yeon, SY, Doh NL (2009) VPass: algorithmic compass using vanishing points in indoor environments. In: IEEE/RSJ international conference on intelligent robots and systems, pp 936–941Google Scholar
  6. 6.
    Antone ME, Teller S (2000) Automatic recovery of relative camera rotations for urban scenes. In: IEEE conference on computer vision and pattern recognition, pp 282–289Google Scholar
  7. 7.
    Košecká J, Zhang W (2002) Video compass. In: European conference on computer vision, pp 476–490Google Scholar
  8. 8.
    Schindler G, Dellaert F (2004) Atlanta world: an expectation maximization framework for simultaneous low-level edge grouping and camera calibration in complex man-made environments. In: IEEE conference on computer vision and pattern recognition, vol 1Google Scholar
  9. 9.
    Martins AT, Aguiar PM, Figueiredo MA (2005) Orientation in manhattan: equiprojective classes and sequential estimation. IEEE Trans Pattern Anal Mach Intell 27(5):822–827CrossRefGoogle Scholar
  10. 10.
    Bazin J-C, Demonceaux C, Vasseur P, Kweon I (2012) Rotation estimation and vanishing point extraction by omnidirectional vision in urban environment. Int J Robot Res 31(1):63–81CrossRefGoogle Scholar
  11. 11.
    Beall C, Ta DN, Ok K, Dellaert F (2012) Attitude heading reference system with rotation-aiding visual landmarks. In: International conference on information fusion, pp 976–982Google Scholar
  12. 12.
    Kawanishi R, Yamashita A, Kaneko T, Asama H (2012) Line-based camera movement estimation by using parallel lines in omnidirectional video. In: IEEE international conference on robotics and automation. IEEE, Saint Paul, MN, USAGoogle Scholar
  13. 13.
    Elloumi W, Guissous K, Chetouani A, Canals R, Leconge R, Emile B, Treuillet S (2013) Indoor navigation assistance with a smartphone camera based on vanishing points. In: International conference on indoor positioning and indoor navigation. IEEE, Montbeliard-Belfort, FranceGoogle Scholar
  14. 14.
    Bosse M, Rikoski R, Leonard J, Teller S (2003) Vanishing points and three-dimensional lines from omni-directional video. Vis Comput 19(6):417–430CrossRefGoogle Scholar
  15. 15.
    Zhang G, Kang DH, Suh IH (2012) Loop closure through vanishing points in a line-based monocular SLAM. In: IEEE international conference on robotics and automation. IEEE, Saint Paul, MN, USA, pp 4565–4570Google Scholar
  16. 16.
    Scheggi S, Morbidi F, Prattichizzo D (2013) Uncalibrated visual compass from omnidirectional line images with application to attitude MAV estimation. In: IEEE/RSJ international conference on intelligent robots and systems. IEEE, Tokyo, Japan, pp 1602–1607Google Scholar
  17. 17.
    Camposeco F, Pollefeys M (2015) Using vanishing points to improve visual-inertial odometry. In: IEEE international conference on robotics and automation. Seattle, Washington, pp 5219–5225Google Scholar
  18. 18.
    Yeon S, Jun C, Choi H, Kang J, Yun Y, Doh NL. Robust-PCA-based hierarchical plane extraction for application to geometric 3D indoor mapping. Industrial Robot (accepted)Google Scholar
  19. 19.
    Choi Y, Lee T, Oh SY (2008) A line feature based SLAM with low grade range sensors using geometric constraints and active exploration. Auton Robots 24:13–27CrossRefGoogle Scholar
  20. 20.
    Pfister ST (2006) Algorithms for mobile robot localization and mapping, incorporating detailed noise modeling and multi-scale feature extraction. PhD thesis, California Institute of TechnologyGoogle Scholar
  21. 21.
    Cheon YJ, Kim JH (2007) Unscented filtering in a unit quaternion space for spacecraft attitude estimation. In: IEEE international symposium on industrial electronics (ISIE). IEEE, Vigo, Spain, pp 66–71Google Scholar
  22. 22.
    Dissanayake M, Newman P, Clark S, Durrant-Whyte H, Csorba M (2001) A solution to the simultaneous localization and map building problem. IEEE Trans Robot Autom 17(3):229–241CrossRefGoogle Scholar
  23. 23.
    Huang GP, Mourikis A, Roumeliotis SI (2010) Observability-based rules for designing consistent EKF SLAM estimators. Int J Robot Res 29(5):502–528CrossRefGoogle Scholar
  24. 24.
    Corke P (2011) Robotics, vision and control. Springer, BerlinCrossRefzbMATHGoogle Scholar
  25. 25.
  26. 26.
    Julier SJ, Uhlmann JK, Durrant-Whyte H (2000) A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Trans Autom Control 45(3):477–482MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Howard A, Roy N (2003) The robotics data set repository (radish)Google Scholar
  28. 28.
    Kümmerle R, Steder B, Dornhege, C, Ruhnke M, Grisetti G, Stachniss C, Kleiner V (2009) Slam benchmarking webpageGoogle Scholar
  29. 29.
    Bonarini A, Burgard W, Fontana G, Matteucci M, Sorrenti DG, Tardós JD (2006) Rawseeds: robotics advancement through web-publishing of sensorial and elaborated extensive data sets. In: IEEE/RSJ international conference on intelligent robots and systemsGoogle Scholar
  30. 30.
  31. 31.
    Kümmerle R, Steder B, Dornhege C, Ruhnke M, Grisetti G, Stachniss C, Kleiner A (2009) On measuring the accuracy of SLAM algorithms. Auton Robots 27:387–407CrossRefGoogle Scholar
  32. 32.
  33. 33.
    Guivant JE, Nebot EM (2001) Optimization of the simultaneous localization and map-building algorithm for real-time implementation. IEEE Trans Robot Autom 17(3):242–257CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Keonyong Lee
    • 1
  • Soo-Hyun Ryu
    • 1
  • Changjoo Nam
    • 2
  • Nakju Lett Doh
    • 1
    • 3
    Email author
  1. 1.School of Electrical Engineering at Korea UniversitySeoulKorea
  2. 2.The Robotics Institute at Carnegie Mellon UniversityPittsburghUSA
  3. 3.CEO, TeeVR Inc.SeoulSouth Korea

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