Global Registration of Point Clouds for Mapping

  • Carlos SánchezEmail author
  • Simone Ceriani
  • Pierluigi Taddei
  • Erik Wolfart
  • Vítor Sequeira
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)


We present a robust Global Registration technique focused on environment survey applications using laser range-finders. Our approach works under the assumption that places can be recognized by analyzing the projection of the observed points along the gravity direction. Candidate 3D matches are estimated by aligning the 2D projective representations of the acquired scans, and benefiting from the corresponding dimensional reduction. Each single candidate match is then validated exploiting the implicit empty space information associated to scans. The global reconstruction problem is modeled as a directed graph, where scan poses (nodes) are connected through matches (edges). This is exploited to compute local matches (instead of global ones) between pairs of scans that are in the same reference frame. As a consequence, both performance and recall ratio increase w.r.t. using only global matches. Additionally, the graph structure allows formulating a sparse global optimization problem that optimizes scan poses, considering simultaneously all accepted matches. Our approach is being used in production systems and has been successfully evaluated on several real datasets.


Global registration Loop detection Place recognition SLAM 


  1. 1.
    Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  2. 2.
    Bosse, M., Zlot, R.: Place recognition using keypoint voting in large 3d lidar datasets. In: ICRA (2013)Google Scholar
  3. 3.
    Calonder, M., Lepetit, V., Ozuysal, M., Trzcinski, T., Strecha, C., Fua, P.: Brief: computing a local binary descriptor very fast. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1281–1298 (2012)CrossRefGoogle Scholar
  4. 4.
    Collier, J., Se, S., Kotamraju, V., Jasiobedzki, P.: Real-time lidar-based place recognition using distinctive shape descriptors. In: SPIE Unmanned Systems Technology (2012)Google Scholar
  5. 5.
    Cummins, M., Newman, P.: Appearance-only slam at large scale with fab-map 2.0. Int. J. Robot. Res. 30(9), 1100–1123 (2011)CrossRefGoogle Scholar
  6. 6.
    Filliat, D.: A visual bag of words method for interactive qualitative localization and mapping. In: Proceedings 2007 IEEE International Conference on Robotics and Automation, pp. 3921–3926, April 2007Google Scholar
  7. 7.
    Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: CVPR (2012)Google Scholar
  8. 8.
    Ho, K.L., Newman, P.: Detecting loop closure with scene sequences. Int. J. Comput. Vis. 74(3), 261–286 (2007)CrossRefGoogle Scholar
  9. 9.
    Košecká, J., Li, F., Yang, X.: Global localization and relative positioning based on scale-invariant keypoints. Robot. Auton. Syst. 52(1), 27–38 (2005)CrossRefGoogle Scholar
  10. 10.
    Kümmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: G2o: a general framework for graph optimization. In: ICRA (2011)Google Scholar
  11. 11.
    Lee, C.H., Varshney, A., Jacobs, D.W.: Mesh saliency. ACM Trans. Graph. 24(3), 659–666 (2005)CrossRefGoogle Scholar
  12. 12.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV (1999)Google Scholar
  13. 13.
    Lynen, S., Bosse, M., Furgale, P., Siegwart, R.: Placeless place-recognition. In: 3DV (2014)Google Scholar
  14. 14.
    Magnusson, M., Andreasson, H., Nüchter, A., Lilienthal, A.J.: Automatic appearance-based loop detection from three-dimensional laser data using the normal distributions transform. J. Field Robot. 26(11–12), 892–914 (2009)CrossRefGoogle Scholar
  15. 15.
    Muhammad, N., Lacroix, S.: Loop closure detection using small-sized signatures from 3d lidar data. In: 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics, pp. 333–338, November 2011Google Scholar
  16. 16.
    Newman, P., Sibley, G., Smith, M., Cummins, M., Harrison, A., Mei, C., Posner, I., Shade, R., Schroeter, D., Murphy, L., Churchill, W., Cole, D., Reid, I.: Navigating, recognizing and describing urban spaces with vision and lasers. Int. J. Rob. Res. 28(11–12), 1406–1433 (2009)CrossRefGoogle Scholar
  17. 17.
    Paul, R., Newman, P.: Fab-map 3d: Topological mapping with spatial and visual appearance. In: ICRA (2010)Google Scholar
  18. 18.
    Rusu, R.B., Cousins, S.: 3d is here: Point cloud library (PCL). In: ICRA (2011)Google Scholar
  19. 19.
    Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: ICRA (2009)Google Scholar
  20. 20.
    Sánchez-Belenguer, C., Vendrell-Vidal, E.: An efficient technique to recompose archaeological artifacts from fragments. In: VSMM (2014)Google Scholar
  21. 21.
    Schindler, G., Brown, M., Szeliski, R.: City-scale location recognition. In: CVPR, pp. 1–7 (2007)Google Scholar
  22. 22.
    Shi, J., Tomasi, C.: Good features to track. In: CVPR (1994)Google Scholar
  23. 23.
    Sipiran, I., Bustos, B.: Harris 3D: a robust extension of the harris operator for interest point detection on 3D meshes. Vis. Comput. 27(11), 963–976 (2011)CrossRefGoogle Scholar
  24. 24.
    Steder, B., Ruhnke, M., Grzonka, S., Burgard, W.: Place recognition in 3D scans using a combination of bag of words and point feature based relative pose estimation. In: IROS (2011)Google Scholar
  25. 25.
    Steder, B., Rusu, R.B., Konolige, K., Burgard, W.: Point feature extraction on 3D range scans taking into account object boundaries. In: ICRA (2011)Google Scholar
  26. 26.
    Taddei, P., Sánchez, C., Rodríguez, A.L., Ceriani, S., Sequeira, V.: Detecting ambiguity in localization problems using depth sensors. In: 3DV (2014)Google Scholar
  27. 27.
    Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: ECCV (2010)Google Scholar
  28. 28.
    Yao, J., Ruggeri, M., Taddei, P., Sequeira, V.: Robust surface registration using n-points approximate congruent sets. EURASIP J. Adv. Signal Process. (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Carlos Sánchez
    • 1
    Email author
  • Simone Ceriani
    • 1
  • Pierluigi Taddei
    • 1
  • Erik Wolfart
    • 1
  • Vítor Sequeira
    • 1
  1. 1.European CommissionJoint Research Centre (JRC)IspraItaly

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