Semi-semantic Line-Cluster Assisted Monocular SLAM for Indoor Environments

  • Ting SunEmail author
  • Dezhen Song
  • Dit-Yan Yeung
  • Ming Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)


This paper presents a novel method to reduce the scale drift for indoor monocular simultaneous localization and mapping (SLAM). We leverage the prior knowledge that in the indoor environment, the line segments form tight clusters, e.g. many door frames in a straight corridor are of the same shape, size and orientation, so the same edges of these door frames form a tight line segment cluster. We implement our method in the popular ORB-SLAM2, which also serves as our baseline. In the front end we detect the line segments in each frame and incrementally cluster them in the 3D space. In the back end, we optimize the map imposing the constraint that the line segments of the same cluster should be the same. Experimental results show that our proposed method successfully reduces the scale drift for indoor monocular SLAM.


SLAM Monocular Indoor Line-segment Clustering 


  1. 1.
    Beevers, K.R., Huang, W.H.: Inferring and enforcing relative constraints in SLAM. In: Akella, S., Amato, N.M., Huang, W.H., Mishra, B. (eds.) Algorithmic Foundation of Robotics VII. STAR, vol. 47, pp. 139–154. Springer, Heidelberg (2008). Scholar
  2. 2.
    Botterill, T., Mills, S., Green, R.: Correcting scale drift by object recognition in single-camera SLAM. IEEE Trans. Cybern. 43(6), 1767–1780 (2013)CrossRefGoogle Scholar
  3. 3.
    Case, C., Suresh, B., Coates, A., Ng, A.Y.: Autonomous sign reading for semantic mapping. In: ICRA, pp. 3297–3303. IEEE (2011)Google Scholar
  4. 4.
    Civera, J., Gálvez-López, D., Riazuelo, L., Tardós, J.D., Montiel, J.: Towards semantic SLAM using a monocular camera. In: IROS, pp. 1277–1284. IEEE (2011)Google Scholar
  5. 5.
    De Boor, C., De Boor, C., Mathématicien, E.U., De Boor, C., De Boor, C.: A Practical Guide to Splines, vol. 27. Springer, New York (1978)CrossRefGoogle Scholar
  6. 6.
    Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 834–849. Springer, Cham (2014). Scholar
  7. 7.
    Fioraio, N., Di Stefano, L.: Joint detection, tracking and mapping by semantic bundle adjustment. In: CVPR, pp. 1538–1545. IEEE (2013)Google Scholar
  8. 8.
    Gálvez-López, D., Salas, M., Tardós, J.D., Montiel, J.: Real-time monocular object SLAM. Robot. Auton. Syst. 75, 435–449 (2016)CrossRefGoogle Scholar
  9. 9.
    Kostavelis, I., Charalampous, K., Gasteratos, A., Tsotsos, J.K.: Robot navigation via spatial and temporal coherent semantic maps. Eng. Appl. Artif. Intell. 48, 173–187 (2016)CrossRefGoogle Scholar
  10. 10.
    Kostavelis, I., Gasteratos, A.: Semantic mapping for mobile robotics tasks: a survey. Robot. Auton. Syst. 66, 86–103 (2015)CrossRefGoogle Scholar
  11. 11.
    Kümmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: G2O: a general framework for graph optimization. In: ICRA, pp. 3607–3613. IEEE (2011)Google Scholar
  12. 12.
    Lemaire, T., Lacroix, S.: Monocular-vision based SLAM using line segments. In: ICRA, pp. 2791–2796. IEEE (2007)Google Scholar
  13. 13.
    Lu, Y., Song, D.: Robust RGB-D odometry using point and line features. In: ICCV, pp. 3934–3942 (2015)Google Scholar
  14. 14.
    Lu, Y., Song, D.: Visual navigation using heterogeneous landmarks and unsupervised geometric constraints. IEEE Trans. Robot. 31(3), 736–749 (2015)CrossRefGoogle Scholar
  15. 15.
    Lu, Y., Song, D., Yi, J.: High level landmark-based visual navigation using unsupervised geometric constraints in local bundle adjustment. In: ICRA, pp. 1540–1545. IEEE (2014)Google Scholar
  16. 16.
    Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Robot. 31(5), 1147–1163 (2015)CrossRefGoogle Scholar
  17. 17.
    Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017)CrossRefGoogle Scholar
  18. 18.
    Nützi, G., Weiss, S., Scaramuzza, D., Siegwart, R.: Fusion of IMU and vision for absolute scale estimation in monocular SLAM. J. Intell. Robot. Syst. 61(1–4), 287–299 (2011)CrossRefGoogle Scholar
  19. 19.
    Parsley, M.P., Julier, S.J.: Towards the exploitation of prior information in SLAM. In: IROS, pp. 2991–2996. IEEE (2010)Google Scholar
  20. 20.
    Pumarola, A., Vakhitov, A., Agudo, A., Sanfeliu, A., Moreno-Noguer, F.: PL-SLAM: real-time monocular visual SLAM with points and lines. In: ICRA, pp. 4503–4508. IEEE (2017)Google Scholar
  21. 21.
    Strasdat, H., Montiel, J., Davison, A.J.: Scale drift-aware large scale monocular SLAM. Robot.: Sci. Syst. VI 2 (2010)Google Scholar
  22. 22.
    Stückler, J., Biresev, N., Behnke, S.: Semantic mapping using object-class segmentation of RGB-D images. In: IROS, pp. 3005–3010. IEEE (2012)Google Scholar
  23. 23.
    Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: IROS, pp. 573–580. IEEE (2012)Google Scholar
  24. 24.
    Tomono, M., Yuta, S.: Mobile robot navigation in indoor environments using object and character recognition. In: ICRA, vol. 1, pp. 313–320. IEEE (2000)Google Scholar
  25. 25.
    Von Gioi, R.G., Jakubowicz, J., Morel, J.M., Randall, G.: LSD: a fast line segment detector with a false detection control. TPAMI 32(4), 722–732 (2010)CrossRefGoogle Scholar
  26. 26.
    Zhang, G., Lee, J.H., Lim, J., Suh, I.H.: Building a 3-D line-based map using stereo SLAM. IEEE Trans. Robot. 31(6), 1364–1377 (2015)CrossRefGoogle Scholar
  27. 27.
    Zhang, G., Suh, I.H.: A vertical and floor line-based monocular SLAM system for corridor environments. IJCAS 10(3), 547–557 (2012)Google Scholar
  28. 28.
    Zhang, J., Song, D.: On the error analysis of vertical line pair-based monocular visual odometry in urban area. In: IROS, pp. 3486–3491. IEEE (2009)Google Scholar
  29. 29.
    Zhang, J., Song, D.: Error aware monocular visual odometry using vertical line pairs for small robots in urban areas. In: AAAI (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ting Sun
    • 1
    Email author
  • Dezhen Song
    • 2
  • Dit-Yan Yeung
    • 3
  • Ming Liu
    • 1
  1. 1.Department of Electronic and Computer EngineeringHong Kong University of Science and TechnologyHong KongChina
  2. 2.Department of Computer Science and EngineeringTexas A&M UniversityCollege StationUSA
  3. 3.Department of Computer ScienceHong Kong University of Science and TechnologyHong KongChina

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