Skip to main content
SpringerLink
Log in

Geometric features for robust registration of point clouds

  • Representation, Processing, Analysis and Understanding of Images
  • Published:
Pattern Recognition and Image Analysis Aims and scope Submit manuscript

Abstract

Several feature detectors for 3D point data have been proposed in the literature. They have been applied to various problems in computer vision and robotics. We use them to solve two fundamental problems in real-time robotics, namely the registration of laser scans as well as the detection of loops and places. We extend and modify existing feature detectors, combine them in a smart way and create a system, that solves these problems efficiently and better that existing other solutions.

We evaluate our system with data sets provided by other groups as well as our own data and we compare our results to those obtained with other algorithms.

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

Similar content being viewed by others

References

  1. Proc. 3rd Int. Conf. on 3D Digital Imaging and Modeling (3DIM 2001) (IEEE Computer Soc., Quebec City, 2001).

    Google Scholar 

  2. L. A. Alexandre, “3D descriptors for object and category recognition: a comparative evaluation,” in Proc. Workshop on ColorDepth Camera Fusion in Robotics at the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) (Vilamoura, 2012).

    Google Scholar 

  3. M. Cummins and P. Newman, “Fabmap: Probabilistic localization and mapping in the space of appearance,” Int. J. Robotics Res. 27 (6), 647–665 (2008).

    Article  Google Scholar 

  4. A. Frome, D. Huber, R. Kolluri, T. Bülow, and J. Malik, “Recognizing objects in range data sing regional point descriptors,” in Proc. Europ. Conf. on Computer Vision (ECCV), Lecture Notes in Computer Science, Ed. by T. Pajdla and J. Matas (Springer, Berlin, Heidelberg, 2004), Vol. 3023, pp. 224–237.

    Google Scholar 

  5. G. Grisetti, R. Kümmerle, C. Stachniss, U. Frese, and C. Hertzberg, “Hierarchical optimization on manifolds for online 2D and 3D mapping,” in Proc. IEEE Int. Conf. on Robotics and Automation (ICRA) (Anchorage, AK, 2010).

    Google Scholar 

  6. C. Harris and M. Stephens, “A combined corner and edge detector,” in Proc. 4th Alvey Vision Conf. (Manchester, 1988), pp. 147–151.

    Google Scholar 

  7. S. Holzer, J. Shotton, and P. Kohli, “Learning to efficiently detect repeatable interest points in depth data,” in Proc. 12th European Conf. on Computer Vision (ECCV’12) (SpringerVerlag, Berlin, Heidelberg, 2012), pp. 200–213.

    Google Scholar 

  8. S. Izadi, D. Kim, O. Hilliges, D. Molyneaux, R. Newcombe, P. Kohli, J. Shotton, S. Hodges, D. Freeman, A. Davison, et al., “Kinectfusion: Realtime 3D reconstruction and interaction using a moving depth camera,” in Proc. 24th Annu. ACM Symp. on User Interface Software and Technology (Santa Barbara, 2011), pp. 559–568.

    Google Scholar 

  9. R. Kümmerle, G. Grisetti, H. Strasdat, K. Konolige, and W. Burgard, “G2O: A general framework for graph optimization,” in Proc. Int. Conf. on Robotics and Automation (ICRA) (Shanghai, 2011), pp. 3607–3613.

    Google Scholar 

  10. R. Kümmerle, B. Steder, C. Dornhege, M. Ruhnke, G. Grisetti, C. Stachniss, and A. Kleiner, “On measuring the accuracy of slam algorithms,” Autonom. Robots 27 (6), 387–407 (2009)

    Article  Google Scholar 

  11. X. Li and I. Guskov, “Multiscale features for approximate alignment of pointbased surfaces,” in EG Symposium on Geometry Processing, Ed. by M. Desbrun and H. Pottmann (Eurographics Assoc., Vienna, 2005), pp. 217–226.

    Google Scholar 

  12. E. Olson, “Realtime correlative scan matching,” in Proc. IEEE Int. Conf. on Robotics and Automation (Kobe, 2009), pp. 4387–4393.

    Google Scholar 

  13. E. Olson and P. Agarwal, “Inference on networks of mixtures for robust robot mapping,” in Proc. Conf. on Robotics: Science and Systems (RSS) (Sydney, 2012).

    Google Scholar 

  14. E. Olson and Y. Li, “IPJC: The incremental posterior joint compatibility test for fast feature cloud matching,” in Proc. IEEE Int. Conf. on Intelligent Robots and Systems (IROS) (Vilamoura-Algarve, 2012).

    Google Scholar 

  15. M. Ruhnke, R. Kümmerle, G. Grisetti, and W. Burgard, “Highly accurate 3D surface models by sparse surface adjustment,” in Proc. IEEE Int. Conf. on Robotics and Automation (ICRA) (St. Paul, MN, 2012), pp. 751–757.

    Google Scholar 

  16. S. Rusinkiewicz and M. Levoy, “Efficient variants of the ICP algorithm,” in Proc. Int. Conf. on 3D Digital Imaging and Modeling (Quebec City, 2001), pp. 145–152.

    Chapter  Google Scholar 

  17. R. B. Rusu, N. Blodow, and M. Beetz, “Fast point feature histograms (FPFH) for 3D registration,” in Proc. IEEE Int. Conf. on Robotics and Automation (ICRA) (Kobe, 2009), pp. 3212–3217.

    Google Scholar 

  18. R. B. Rusu, N. Blodow, Z. C. Marton, and M. Beetz, “Aligning point cloud views using persistent feature histograms,” in Proc. IEEE Int. Conf. on Intelligent Robots and Systems (IROS) (Nice, 2008), pp. 3384–3391.

    Google Scholar 

  19. R. B. Rusu, G. Bradski, R. Thibaux, and J. Hsu, “Fast 3D recognition and pose using the viewpoint feature histogram,” in Proc. Int. Conf. on Intelligent Robots and Systems (IROS) (Taipei, 2010), pp. 2155–2162.

    Google Scholar 

  20. R. B. Rusu and S. Cousins, “3D is here: Point cloud library (PCL),” in Proc. 2011 IEEE Int. Conf. on Robotics and Automation (ICRA) (Shanghai, 2011), pp. 1–4.

    Chapter  Google Scholar 

  21. I. Sipiran and B. Bustos, “Harris 3D: A robust extension of the Harris operator for interest point detection on 3D meshes,” Visual Comput. 27 (11), 963–976 (2011).

    Article  Google Scholar 

  22. B. Steder, G. Grisetti, and W. Burgard, “Robust place recognition for 3D range data based on point features,” in Proc. IEEE Int. Conf. on Robotics and Automation (ICRA) (Anchorage, AK, 2010), pp. 1400–1405.

    Google Scholar 

  23. B. Steder, M. Ruhnke, S. Grzonka, and W. Burgard, “Place recognition in 3D scans using a combination of bag of words and point feature based relative pose estimation,” in Proc. Int. Conf. on Intelligent Robots and Systems (IROS) (San Francisco, 2011).

    Google Scholar 

  24. B. Steder, R. B. Rusu, K. Konolige, and W. Burgard, “Narf: 3D range image features for object recognition,” in Workshop on Defining and Solving Realistic Perception Problems in Personal Robotics at the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) (Taipei, 2010).

    Google Scholar 

  25. B. Steder, R. B. Rusu, K. Konolige, and W. Burgard, “Point feature extraction on 3D range scans taking into account object boundaries,” in Proc. IEEE Int. Conf. on Robotics and Automation (ICRA) (Shanghai, 2011), pp. 2601–2608.

    Google Scholar 

  26. J. Stückler and S. Behnke, “Interest point detection in depth images through scalespace surface analysis,” in Proc. Int. Conf. on Robotics and Automation (ICRA) (Shanghai, 2011), pp. 3568–3574.

    Google Scholar 

  27. F. Tombari and L. Di Stefano, “Object recognition in 3D scenes with occlusions and clutter by hough voting,” in Proc. 4th Pacificim Symposium on Image and Video Technology (PSIVT) (Singapore, 2010), pp. 349–355.

    Google Scholar 

  28. F. Tombari, S. Salti, and L. Di Stefano, “Unique shape context for 3D data description,” in Proc. of the ACM Workshop on 3D Object Retrieval (3DOR) (New York, 2010), pp. 57–62.

    Chapter  Google Scholar 

  29. F. Tombari, S. Salti, and L. Di Stefano, “Unique signatures of histograms for local surface description,” in Proc. Europ. Conf. on Computer Vision (ECCV’10) (Springer-Verlag, Berlin, Heidelberg, 2010), pp. 356–369.

    Google Scholar 

  30. F. Tombari, S. Salti, and L. Di Stefano, “A combined textureshape descriptor for enhanced 3D feature matching,” in Proc. IEEE Int. Conf. on Image Processing (ICIP) (Brussels, 2011), pp. 809–812.

    Google Scholar 

  31. A. Zaharescu, E. Boyer, K. Varanasi, and R. Horaud, “Surface feature detection and description with applications to mesh matching,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (Miami Beach, Florida, 2009), pp. 373–380.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Active Vision Group, AG AS, Institute for Computational Visualistics, University of Koblenz-Landau, Landau, Germany

    A. Mützel, F. Neuhaus & D. Paulus

Authors
  1. A. Mützel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. Neuhaus
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D. Paulus
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Mützel.

Additional information

This paper uses the materials of the report submitted at the 11th International Conference “Pattern Recognition and Image Analysis: New Information Technologies,” Samara, Russia, September 23–28, 2013.

The article is published in the original.

Dietrich Paulus, born 1959, obtained a Bachelor degree in Computer Science from University of Western Ontario, London, Canada, followed by a diploma (Dipl.-Inf.) in Computer Science and a PhD (Dr.-Ing.) from Friedrich–Alexander University Erlangen-Nuremberg, Germany. He obtained his habilitation in Erlangen in 2001. Since 2001 he is at the Institute for Computational Visualistics at the University Koblenz–Landau, Germany where he became a full professor in 2002. His primary interests are computer vision and robot vision.

Andreas Mützel received a Bachelors degree in Computational Visualistics from the University of Koblenz–Landau, and graduated with a Masters Degree with honors in the same subject in 2013. He worked in the active vision group as a student assistant. His research interests were 3D mapping, especially in combination with 3D features. Since his graduation, he works with RV Realtime Visions GmbH, Koblenz.

Frank Neuhaus received a Diploma degree in Computer Science from the University of Koblenz–Landau where he graduated with honors in 2011. Since then he works as a research associate in the active vision group. His research is focused on 3D mapping and probabilistic modeling.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mützel, A., Neuhaus, F. & Paulus, D. Geometric features for robust registration of point clouds. Pattern Recognit. Image Anal. 25, 174–186 (2015). https://doi.org/10.1134/S1054661815020182

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1054661815020182

Keywords

Search

Navigation