Succinct Landmark Database

  • Kanji Tanaka
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 193)


Recently developed robotic mapping techniques enable the acquisition of large scale landmark databases. This paper explores an approach for succinct landmark database, which memorizes a large collection of point landmarks while allowing to random access the location of i-th landmark. Our approach combines and extends three independent compression techniques: space coding, succinct data structure, and exemplar-based scene compression. Experiments using real datasets evaluate effectiveness of the presented techniques in terms of compactness, access speed, and accuracy of landmark database.


landmark database space coding succinct data structure exemplar-based scene compression 


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  1. 1.
    Angeli, A., Filliat, D., Doncieux, S., Meyer, J.A.: A fast and incremental method for loop-closure detection using bags of visual words. IEEE Transactions on Robotics, Special Issue on Visual SLAM (2008) (conditionally accpeted for publication)Google Scholar
  2. 2.
    Cummins, M., Newman, P.: Accelerated appearance-only SLAM. In: Proc. IEEE International Conference on Robotics and Automation (2008)Google Scholar
  3. 3.
    Williams, B., Klein, G., Reid, I.: Real-time slam relocalisation. In: Proc. IEEE 11th Int. Conf. Computer Vision, pp. 1–8 (2007)Google Scholar
  4. 4.
    Ila, V., Ni, K., Dellaert, F., Carlson, J., Thorpe, C.E.: Subgraph-preconditioned conjugate gradients for large scale slam. In: Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, IROS (2010)Google Scholar
  5. 5.
    Zhou, Q.-Y., Neumann, U.: 2.5d building modeling with topology control, pp. 2489–2496 (2011)Google Scholar
  6. 6.
    Brown, M.Z., Burschka, D., Hager, G.D.: Advances in computational stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 993–1008 (2003)CrossRefGoogle Scholar
  7. 7.
    Zhou, Q.-Y., Neumann, U.: A streaming framework for seamless building reconstruction from large-scale aerial lidar data, pp. 2759–2766 (2009)Google Scholar
  8. 8.
    Wonka, P., Wimmer, M., Sillion, F., Ribarsky, W.: Instant architecture. ACM Transaction on Graphics 22(3), 669–677 (2003)CrossRefGoogle Scholar
  9. 9.
    Pfister, H., Zwicker, M., Van Baar, J., Gross, M.: Surfels: Surface elements as rendering primitives. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, pp. 335–342 (2000)Google Scholar
  10. 10.
    Lafarge, F., Keriven, R., Brédif, M., Vu, H.: Hybrid multi-view reconstruction by Jump-Diffusion. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 350–357 (2010)Google Scholar
  11. 11.
    Schnabel, R., Klein, R.: Octree-based point-cloud compression (2006)Google Scholar
  12. 12.
    Gumhold, S., Karni, Z., Isenburg, M., Seidel, H.-P.: Predictive point-cloud compression (2005)Google Scholar
  13. 13.
    Botsch, M., Pajarola, R., (eds), Hubo, E., Mertens,T., Haber, T.: Self-similarity-based compression of point clouds, with application to ray tracing abstract (2007)Google Scholar
  14. 14.
    Sagan, H.: Space-filling curves. Springer (1994)Google Scholar
  15. 15.
    Hudson, B.: Succinct representation of well-spaced point clouds. CoRR, abs/0909.3137 (2009)Google Scholar
  16. 16.
    Brisaboa, N.R., Ladra, S., Navarro, G.: Directly addressable variable-length codes, pp. 122–130 (2009)Google Scholar
  17. 17.
    Tomoni, N., Kanji, T.: Dictionary-based map compression for sparse feature maps. In: Proc. IEEE Int. Conf. Robotics and Automation, ICRA (2011)Google Scholar
  18. 18.
    Cham, T.J., Ciptadi, A., Tan, W.C., Pham, M.T., Chia, L.T.: Estimating camera pose from a single urban ground-view omnidirectional image and a 2d building outline map (2010)Google Scholar
  19. 19.
    Kensuke, K., Kanji, T., Tomomi, N.: Grammar-based map compression using manhattan-world priors. In: Proc. IEEE Int. Conf. Robotics and Biomimetics, ROBIO (2011)Google Scholar
  20. 20.
    Tomomi, N., KanjiAn, T.: incremental scheme for dictionary-based compressive slam. In: Proc. IEEE Int. Conf. Intelligent Robots and Systems, IROS (2011)Google Scholar
  21. 21.
    Tomomi, N., Kanji, T.: Dictionary-based map compression using geometric priors. In: Proc. IEEE Int. Conf. Robotics and Biomimetics, ROBIO (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Dept. of Human & Artificial Intelligent SystemsUniv. of FukuiFukuiJapan

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