Advertisement

SlamDunk: Affordable Real-Time RGB-D SLAM

  • Nicola FioraioEmail author
  • Luigi Di Stefano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8925)

Abstract

We propose an effective, real-time solution to the RGB-D SLAM problem dubbed SlamDunk. Our proposal features a multi-view camera tracking approach based on a dynamic local map of the workspace, enables metric loop closure seamlessly and preserves local consistency by means of relative bundle adjustment principles. SlamDunk requires a few threads, low memory consumption and runs at 30 Hz on a standard desktop computer without hardware acceleration by a GPGPU card. As such, it renders real-time dense SLAM affordable on commodity hardware. SlamDunk permits highly responsive interactive operation in a variety of workspaces and scenarios, such as scanning small objects or densely reconstructing large-scale environments. We provide quantitative and qualitative experiments in diverse settings to demonstrate the accuracy and robustness of the proposed approach.

Keywords

RGB-D SLAM Real time SLAM Relative bundle adjustment Camera Tracking 

References

  1. 1.
    Arun, K.S., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-D point sets. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) 9(5), 698–700 (1987)Google Scholar
  2. 2.
    Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (SURF). Computer Vision and Image Understanding 110(3), 346–359 (2008)Google Scholar
  3. 3.
    Borrmann, D., Elseberg, J., Lingemann, K., Nüchter, A., Hertzberg, J.: Globally consistent 3D mapping with scan matching. Journal of Robotics and Autonomous Systems 56, 130–142 (2008)CrossRefGoogle Scholar
  4. 4.
    Bradski, G.: Dr. Dobb’s Journal of Software ToolsGoogle Scholar
  5. 5.
    Bylow, E., Sturm, J., Kerl, C., Kahl, F., Cremers, D.: Real-time camera tracking and 3D reconstruction using signed distance functions. In: Robotics: Science and Systems (RSS), Berlin, Germany (2013)Google Scholar
  6. 6.
    Davison, A., Reid, I.D., Molton, N.D., Stasse, O.: MonoSLAM: Real-time single camera SLAM. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) 29(6), 1052–1067 (2007)Google Scholar
  7. 7.
    Dryanovski, I., Valenti, R., Xiao, J.: Fast visual odometry and mapping from RGB-D data. In: IEEE Int’l Conf. on Robotics and Automation (ICRA), pp. 2305–2310 (May 2013)Google Scholar
  8. 8.
    Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D., Burgard, W.: An evaluation of the RGB-D SLAM system. In: IEEE Int’l Conf. on Robotics and Automation (ICRA). St. Paul, MA (May 2012)Google Scholar
  9. 9.
    Fioraio, N., Konolige, K.: Realtime visual and point cloud SLAM. In: RSS Workshop on RGB-D: Advanced Reasoning with Depth Cameras, Los Angeles (CA), USA (2011)Google Scholar
  10. 10.
    Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: Using kinect-style depth cameras for dense 3D modeling of indoor environments. The International Journal of Robotics Research 31(5), 647–663 (2012)CrossRefGoogle Scholar
  11. 11.
    Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: An efficient probabilistic 3D mapping framework based on octrees. Autonomous Robots (2013). http://octomap.github.com
  12. 12.
    Kerl, C., Sturm, J., Cremers, D.: Dense visual SLAM for RGB-D cameras. In: IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems (2013)Google Scholar
  13. 13.
    Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: IEEE and ACM Int’l Symp on Mixed and Augmented Reality (ISMAR), pp. 225–234 (November 2007)Google Scholar
  14. 14.
    Konolige, K., Agrawal, M.: FrameSLAM: From bundle adjustment to real-time visual mapping. IEEE Transactions on Robotics 24(5), 1066–1077 (2008)CrossRefGoogle Scholar
  15. 15.
    Kümmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: g2o: A general framework for graph optimization. In: International Conference on Robotics and Automation (ICRA), Shanghai, China (May 2011)Google Scholar
  16. 16.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–119 (2004)CrossRefGoogle Scholar
  17. 17.
    Montemerlo, M., Thrun, S.: Large-scale robotic 3-D mapping of urban structures. In: International Symposium on Experimental Robotics (ISER), Singapore (2004)Google Scholar
  18. 18.
    Newcombe, R., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A., Kohli, P., Shotton, J., Hodges, S., Fitzgibbon, A.: KinectFusion: Real-time dense surface mapping and tracking. In: 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Washington, DC, USA, pp. 127–136 (2011)Google Scholar
  19. 19.
    Newcombe, R., Lovegrove, S., Davison, A.: DTAM: Dense tracking and mapping in real-time. In: International Conference on Computer Vision (ICCV), pp. 2320–2327 (November 2011)Google Scholar
  20. 20.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: An efficient alternative to sift or surf. In: IEEE Int’l Conf. on Computer Vision (ICCV), pp. 2564–2571 (November 2011)Google Scholar
  21. 21.
    Scherer, S., Zell, A.: Efficient onbard RGBD-SLAM for autonomous MAVs. In: 2013 IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems (IROS), pp. 1062–1068 (November 2013)Google Scholar
  22. 22.
    Sibley, G., Mei, C., Reid, I., Newman, P.: Adaptive relative bundle adjustment. In: Robotics: Science and Systems (RSS), Seattle, USA (2009)Google Scholar
  23. 23.
    Steinbruecker, F., Kerl, C., Sturm, J., Cremers, D.: Large-scale multi-resolution surface reconstruction from RGB-D sequences. In: International Conference on Computer Vision, Sydney, Australia (2013)Google Scholar
  24. 24.
    Strasdat, H., Davison, A.J., Montiel, J., Konolige, K.: Double window optimisation for constant time visual SLAM. In: International Conference on Computer Vision (ICCV), Los Alamitos, CA, USA, pp. 2352–2359 (2011)Google Scholar
  25. 25.
    Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: Proc. of the International Conference on Intelligent Robot Systems (IROS) (October 2012)Google Scholar
  26. 26.
    Whelan, T., Mcdonald, J., Kaess, M., Fallon, M., Johannsson, H., Leonard, J.: Kintinuous: Spatially extended KinectFusion. In: RSS Workshop on RGB-D: Advanced Reasoning with Depth Cameras, Sydney, Australia (2012)Google Scholar
  27. 27.
    Wu, C.: SiftGPU: A GPU implementation of scale invariant feature transform (SIFT) (2007). http://cs.unc.edu/ccwu/siftgpu

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.CVLab - Department of Computer Science and EngineeringUniversity of BolognaBolognaItaly

Personalised recommendations