Sparse 3D Point-Cloud Map Upsampling and Noise Removal as a vSLAM Post-Processing Step: Experimental Evaluation

  • Andrey BokovoyEmail author
  • Konstantin Yakovlev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11097)


The monocular vision-based simultaneous localization and mapping (vSLAM) is one of the most challenging problem in mobile robotics and computer vision. In this work we study the post-processing techniques applied to sparse 3D point-cloud maps, obtained by feature-based vSLAM algorithms. Map post-processing is split into 2 major steps: (1) noise and outlier removal and (2) upsampling. We evaluate different combinations of known algorithms for outlier removing and upsampling on datasets of real indoor and outdoor environments and identify the most promising combination. We further use it to convert a point-cloud map, obtained by the real UAV performing indoor flight to 3D voxel grid (octo-map) potentially suitable for path planning.


3D Point-cloud Outlier removal Upsampling vSLAM 3D path planning Sparse map Feature-based vSLAM 



This work was partially supported by the “RUDN University Program 5-100” and by the RFBR project No. 17-29-07053.


  1. 1.
    Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., Leonard, J.: Past, present, and future of simultaneous localization and mapping: towards the robust-perception age. IEEE Trans. Robot. 32(6), 1309–1332 (2016)CrossRefGoogle Scholar
  2. 2.
    Balan, A., Flaks, J., Hodges, S., Isard, M., Williams, O., Barham, P., Izadi, S., Hiliges, O., Molyneaux, D., Kim, D., et al.: Distributed asynchronous localization and mapping for augmented reality, January 13 2015. US Patent 8,933,931Google Scholar
  3. 3.
    Li, R., Liu, J., Zhang, L., Hang, Y.: LIDAR/MEMS IMU integrated navigation (SLAM) method for a small UAV in indoor environments. In: 2014 DGON Inertial Sensors and Systems Symposium (ISS), pp. 1–15. IEEE (2014)Google Scholar
  4. 4.
    Leonard, J.J., Bahr, A.: Autonomous underwater vehicle navigation. In: Dhanak, M.R., Xiros, N.I. (eds.) Springer Handbook of Ocean Engineering, pp. 341–358. Springer, Cham (2016). Scholar
  5. 5.
    Caballero, F., Merino, L., Ferruz, J., Ollero, A.: Vision-based odometry and SLAM for medium and high altitude flying UAVs. J. Intell. Robot. Syst. 54(1–3), 137–161 (2009)CrossRefGoogle Scholar
  6. 6.
    Sazdovski, V., Silson, P.M.: Inertial navigation aided by vision-based simultaneous localization and mapping. IEEE Sens. J. 11(8), 1646–1656 (2011)CrossRefGoogle Scholar
  7. 7.
    Vu, Q., Nguyen, V., Solenaya, O., Ronzhin, A., Mehmet, H.: Algorithms for joint operation of service robotic platform and set of UAVs in agriculture tasks. In: 2017 5th IEEE Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE), pp. 1–6. IEEE (2017)Google Scholar
  8. 8.
    Buyval, A., Afanasyev, I., Magid, E.: Comparative analysis of ROS-based monocular SLAM methods for indoor navigation. In: Proceedings of the Ninth International Conference on Machine Vision (ICMV 2016), pp. 10341–10341 (2017).
  9. 9.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: An efficient alternative to SIFT or SURF. In: 2011 IEEE international conference on Computer Vision (ICCV), pp. 2564–2571. IEEE (2011)Google Scholar
  10. 10.
    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
  11. 11.
    Bokovoy, A., Yakovlev, K.: Enhancing semi-dense monocular vSLAM used for multi-rotor UAV navigation in indoor environment by fusing IMU data. In: The 2018 International Conference on Artificial Life and Robotics (ICAROB 2018), pp. 391–394. ALife Robotics Corporation Ltd. (2018)Google Scholar
  12. 12.
    Newcombe, R.A., Lovegrove, S.J., Davison, A.J.: DTAM: Dense tracking and mapping in real-time. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2320–2327. IEEE (2011)Google Scholar
  13. 13.
    Tateno, K., Tombari, F., Laina, I., Navab, N.: CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction. arXiv preprint arXiv:1704.03489 (2017)
  14. 14.
    Makarov, I., Aliev, V., Gerasimova, O.: Semi-dense depth interpolation using deep convolutional neural networks. In: Proceedings of the 2017 ACM on Multimedia Conference, MM 2017, pp. 1407–1415. ACM, New York (2017)Google Scholar
  15. 15.
    Yakovlev, K., Baskin, E., Hramoin, I.: Grid-based angle-constrained path planning. In: Hölldobler, S., Krötzsch, M., Peñaloza, R., Rudolph, S. (eds.) KI 2015. LNCS (LNAI), vol. 9324, pp. 208–221. Springer, Cham (2015). Scholar
  16. 16.
    Magid, E., Tsubouchi, T., Koyanagi, E., Yoshida, T.: Building a search tree for a pilot system of a rescue search robot in a discretized random step environment. J. Robot. Mechatron. 23(4), 567 (2011)CrossRefGoogle Scholar
  17. 17.
    Makarov, I., Polyakov, P.: Smoothing voronoi-based path with minimized length and visibility using composite bezier curves. In: AIST (Supplement), pp. 191–202 (2016)Google Scholar
  18. 18.
    Buyval, A., Gabdulin, A., Mustafin, R., Shimchik, I.: Deriving overtaking strategy from nonlinear model predictive control for a race car. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), pp. 2623–2628, September 2017Google Scholar
  19. 19.
    Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras. IEEE Trans. Robot. (2017)Google Scholar
  20. 20.
    Kitti vision benchmark suite. Accessed 22 Aug 2018
  21. 21.
    Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: an efficient probabilistic 3d mapping framework based on octrees. Autonom. Robots 34(3), 189–206 (2013)CrossRefGoogle Scholar
  22. 22.
    Rusu, R.B., Marton, Z.C., Blodow, N., Dolha, M., Beetz, M.: Towards 3D point cloud based object maps for household environments. Robot. Autonom. Syst. 56(11), 927–941 (2008)CrossRefGoogle Scholar
  23. 23.
    Reuter, P., Joyot, P., Trunzler, J., Boubekeur, T., Schlick, C.: Surface reconstruction with enriched reproducing kernel particle approximation. In: Eurographics/IEEE VGTC Symposium Proceedings on Point-Based Graphics 2005, pp. 79–87. IEEE (2005)Google Scholar
  24. 24.
    Skinner, B., Vidal Calleja, T., Valls Miro, J., De Bruijn, F., Falque, R.: 3D point cloud upsampling for accurate reconstruction of dense 2.5 D thickness maps. In: Australasian Conference on Robotics and Automation (2014)Google Scholar
  25. 25.
    Rusu, R.B., Cousins, S.: 3D is here: Point cloud library (pcl). In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 1–4. IEEE (2011)Google Scholar
  26. 26.
    Huitl, R., Schroth, G., Hilsenbeck, S., Schweiger, F., Steinbach, E.: TUMindoor: an extensive image and point cloud dataset for visual indoor localization and mapping. In: 2012 19th IEEE International Conference on Image Processing (ICIP), pp. 1773–1776. IEEE (2012)Google Scholar
  27. 27.
    Blanco, J.L., Moreno, F.A., Gonzalez, J.: A collection of outdoor robotic datasets with centimeter-accuracy ground truth. Autonom. Robots 27(4), 327 (2009)CrossRefGoogle Scholar
  28. 28.
    Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 573–580. IEEE (2012)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Peoples Friendship University of Russia (RUDN University)MoscowRussia
  2. 2.Federal Research Center “Computer Science and Control” of Russian Academy of SciencesMoscowRussia
  3. 3.National Research University Higher School of EconomicsMoscowRussia

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