Advertisement

Simultaneous Localization and Mapping in Buildings

  • Martin Werner
Chapter

Abstract

Simultaneous localization and mapping (SLAM) is a family of algorithms to generate map information while positioning the moving target at the same time. SLAM algorithms rely on many elementary algorithms such as feature point tracking, loop detection, random sample consensus, surface simplification, and more. This chapter aims to bring together a closed exposition of these algorithms and their application inside buildings.

Keywords

Point Cloud Optical Flow Scale Invariant Feature Transform Iterative Close Point Iterative Close Point Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Aulinas, J., Petillot, Y.R., Salvi, J., Lladó, X.: The slam problem: a survey. In: CCIA, pp. 363–371. Citeseer (2008)Google Scholar
  2. 2.
    Bailey, T., Nieto, J., Guivant, J., Stevens, M., Nebot, E.: Consistency of the ekf-slam algorithm. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3562–3568. IEEE, New York (2006)Google Scholar
  3. 3.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Grisetti, G., Stachniss, C., Burgard, W.: Nonlinear constraint network optimization for efficient map learning. IEEE Trans. Intell. Transp. Syst. 10(3), 428–439 (2009)CrossRefGoogle Scholar
  5. 5.
    Lucas, B.D., Kanade, T., et al.: An iterative image registration technique with an application to stereo vision. In: IJCAI, vol. 81, pp. 674–679 (1981)Google Scholar
  6. 6.
    Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B., et al.: Fastslam: a factored solution to the simultaneous localization and mapping problem. In: AAAI/IAAI, pp. 593–598 (2002)Google Scholar
  7. 7.
    Olson, E., Leonard, J., Teller, S.: Fast iterative alignment of pose graphs with poor initial estimates. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006, pp. 2262–2269. IEEE, New York (2006)Google Scholar
  8. 8.
    OpenSLAM: Give your algorithms to the community. Online (2014). http://www.openslam.org
  9. 9.
    Rusinkiewicz, S., Levoy, M.: Efficient variants of the icp algorithm. In: Third International Conference on 3-D Digital Imaging and Modeling, 2001. Proceedings, pp. 145–152. IEEE, New York (2001)Google Scholar
  10. 10.
    Thrun, S., Leonard, J.J.: Simultaneous localization and mapping. In: Springer Handbook of Robotics, pp. 871–889. Springer, Berlin/Heidelberg (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Martin Werner
    • 1
  1. 1.Ludwig-Maximilians-Universität MünchenMunichGermany

Personalised recommendations