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Experimental comparison of techniques for localization and mapping using a bearing-only sensor

  • Matthew Deans
  • Martial Hebert
Conference paper
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 271)

Abstract

We present a comparison of an extended Kalman filter and an adaptation of bundle adjustment from computer vision for mobile robot localization and mapping using a bearing-only sensor. We show results on synthetic and real examples and discuss some advantages and disadvantages of the techniques. The comparison leads to a novel combination of the two techniques which results in computational complexity near Kalman filters and performance near bundle adjustment on the examples shown.

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References

  1. [1]
    J. J. Leonard and H. F Durrant-Whyte. Simultaneous map building and localization for an autonomous mobile robot. In IEEE/RSJ International Workshop on Intelligent Robots and Systems IROS’ 91, pages 1442–1447, 1991.Google Scholar
  2. [2]
    F. Lu and E. Milios. Globally consistent range scan alignment for environment mapping. Autonomous Robots, 4(4):333–349, 1997.CrossRefGoogle Scholar
  3. [3]
    K. Chong and L. Kleeman. Large scale sonarray mapping using multiple connected local maps. In International Conference on Field and Service Robotics, pages 278–285, 1997.Google Scholar
  4. [4]
    John J. Leonard and Hans Jabob S. Feder. Decoupled stochastic mapping. Technical Report 99-1, MIT Marine Robotics Laboratory, Cambridge, MA 02139, USA, 1999.Google Scholar
  5. [5]
    Richard I. Hartley. Euclidean reconstruction from uncalibrated views. In Zisserman Mundy and Forsyth, editors, Applications of Invariance in Computer Vision, pages 237–256. Springer Verlag, 1994.Google Scholar
  6. [6]
    B. Triggs, P. McLauchlan, R. Hartley, and A. Fitzgibbon. Bundle adjustment-a modern synthesis. In To appear in Vision Algorithms: Theory & Practice. Springer-Verlag, 2000.Google Scholar
  7. [7]
    T. J. Broida, S. Chandrashekhar, and R. Chellappa. Recursive 3-d motion estimation from a monocular image sequence. IEEE Trans. on Aerospace and Electronic Systems, 26(4):639–656, 1990.CrossRefGoogle Scholar
  8. [8]
    Li Azarbayejani and Alex P. Pentland. Recursive estimation of motion, structure and focal length. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(6):562–575, 1995.CrossRefGoogle Scholar
  9. [9]
    Philip F. McLauchlan. Gauge invariance in projective 3d reconstruction. In Proceedings IEEE Workshop on Multi-View Modeling and Analysis of Visual Scenes (MVIEW’99), pages 37–44, 1999.Google Scholar
  10. [10]
    M.W.M.G Dissanayake and et al. An experimental and theoretical investigation into simultaneous localization and map building (slam). In Proc. 6th International Symposium on Experimental Robotics, pages 171–180, 1999.Google Scholar
  11. [11]
    S. Julier, J. Uhlmann, and H. Durrant-Whyte. A new approach for filtering nonlinear systems. In Proceedings of the 1995 American Controls Conference, pages 1628–1632, 1995.Google Scholar
  12. [12]
    J. K. Uhlmann, S. J. Julier, and M. Csorba. Nondivergent simultaneous map-building and localization using covariance intersection. In SPIE Proceedings: Navigation and Control Technologies for Unmanned Systems II, volume 3087, pages 2–11, 1997.Google Scholar
  13. [13]
    P. Mclauchlan. A batch/recursive algorithm for 3d scene reconstruction. In Proceedings of CVPR 2000, pages II:738–43, 2000.Google Scholar
  14. [14]
    R. P. N. Rao. Robust kalman filters for prediction, recognition, and learning. Technical Report 645, Computer Science Department, University of Rochester, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Matthew Deans
    • 1
  • Martial Hebert
    • 1
  1. 1.Carnegie Mellon UniversityPittsburghUSA

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