A Real-Time Algorithm for Mobile Robot Mapping Based on Rotation-Invariant Descriptors and Iterative Close Point Algorithm

  • A. VokhmintcevEmail author
  • K. Yakovlev
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 661)


Nowadays many algorithms for mobile robot mapping in indoor environments have been created. In this work we use a Kinect 2.0 camera, a visible range cameras Beward B2720 and an infrared camera Flir Tau 2 for building 3D dense maps of indoor environments. We present the RGB-D Mapping and a new fusion algorithm combining visual features and depth information for matching images, aligning of 3D point clouds, a “loop-closure” detection, pose graph optimization to build global consistent 3D maps. Such 3D maps of environments have various applications in robot navigation, real-time tracking, non-cooperative remote surveillance, face recognition, semantic mapping. The performance and computational complexity of the proposed RGB-D Mapping algorithm in real indoor environments is presented and discussed.


Fusion Simultaneous location and mapping Iterative closest point algorithm Matching algorithm Histograms of oriented gradients Depth map 



The work was supported by the RFBR, project no 16-08-00342 and the Ministry of Education and Science of Russian Federation, grant no.2.1766.2014.


  1. 1.
    Hertzberg, C., Wagner, R., Birbach, O.: Experiences in building a visual slam system from open source components. In: Proceedings IEEE International Conference on Robotics and Automation, pp. 2644–2651 (2011)Google Scholar
  2. 2.
    Endres, F., Hess, J., Engelhard, N., Sturm, J.: An evaluation of the RGB-D SLAM system. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1691–1696 (2012)Google Scholar
  3. 3.
    Davison Andrew, J., Reid Ian, D., Molton Nicholas, D., Stasse, O.: MonoSLAM Real-Time Single Camera SLAM. IEEE Trans. Pattern Anal. Mach. Intell. 7, 1052–1067 (2007)CrossRefGoogle Scholar
  4. 4.
    Pollefeys, M., Nister, D., Frahm, J.-M., Akbarzadeh, A., Mordohai, P., Clipp, B., Engels, C.: Detailed real-time Urban 3D reconstruction from video. Int. J. Comput. Vis. 78(2), 143–167 (2008)CrossRefGoogle Scholar
  5. 5.
    Fioraio, N., Konolige, K.: Realtime visual and point cloud SLAM. In: Proceedings of the RSS Workshop on RGB-D: Advanced Reasoning with Depth Cameras at Robotics, no. 27 (2011)Google Scholar
  6. 6.
    Konolige, K., Agrawal, M., Sola, J.: Large scale visual odometry for rough terrain. In: Proceedings of the International Symposium on Robotics Research, 201–212 (2010)Google Scholar
  7. 7.
    Konolige, K., Agrawal, M., Bolles, R.C., Cowan, C., Fischler, M., Gerkey, B.: Outdoor mapping and navigation using stereo vision. In: Proceedings of the International Symposium on Experimental Robotics, pp. 179–190 (2006)Google Scholar
  8. 8.
    Nister, D.: An efficient solution to the five-point relative pose problem. IEEE Trans. Pattern Anal. Mach. Intell. 26, 756–777 (2004)CrossRefGoogle Scholar
  9. 9.
    Snavely, N., Seitz, S., Szeliski, R.: Photo tourism: exploring photo collections in 3D. Proc. ACM Trans. Graphics 25(3), 835–846 (2006)CrossRefGoogle Scholar
  10. 10.
    Besl, P., McKay, N.: A method for registration of 3-D shapes trans. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)CrossRefGoogle Scholar
  11. 11.
    Chen, Y., Medioni, G.: Object modeling by registration of multiple range images. J. Image Vis. Comput. 10(3), 145–155 (1992). ElsevierCrossRefGoogle Scholar
  12. 12.
    Fischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. In: Graphics and Image Processing, pp. 381–395 (1981)Google Scholar
  13. 13.
    Lowe, D.G.: Object recognition from local scale invariant features. In: Proceedings of the 7th International conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)Google Scholar
  14. 14.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. Comput. Vis. Image Underst. 110, 346–359 (2008)CrossRefGoogle Scholar
  15. 15.
    Vokhmintcev, A.V., Sochenkov, I.V., Kuznetsov, V.V., Tikhonkikh, D.V.: Face recognition based on matching algorithm with recursive calculation of local oriented gradient histogram. Dokl. Math. 466(3), 261–266 (2016)zbMATHGoogle Scholar
  16. 16.
    Miramontes-Jaramillo, D., Kober, V., Diaz-Ramirez, V.H., Karnaukhov, V.: A novel image matching algorithm based on sliding histograms of oriented gradients. J. Commun. Technol. Electron. 59(12), 1446–1450 (2014)CrossRefGoogle Scholar
  17. 17.
    Vokhmintsev, A., Makovetskii, A., Kober, V., Sochenkov, I., Kuznetsov, V.: A fusion algorithm for building three-dimensional maps. In: Proceedings. SPIE‘s Annual Meeting: Applications of Digital Image Processing XXXVIII, vol. 8452, p. 9599-81 (2015)Google Scholar
  18. 18.
    Henry, P., Krainin, M., Herbst, E.: RGB-D mapping: Using depth cameras for dense 3D modeling of indoor environments. In: Proceedings of the 12th International Symposium on Experimental Robotics, pp. 477–491 (2014)Google Scholar
  19. 19.
    Josef, S.: Efficient visual search of videos cast as text retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 591–605 (2009)CrossRefGoogle Scholar
  20. 20.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: IEEE International Conference Computer Vision (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Research LaboratoryChelyabinsk State UniversityChelyabinskRussia
  2. 2.Computer Science and Control of Russian Academy of SciencesNational Research University Higher School of EconomicsMoscowRussia

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