Advertisement

Augmenting Mobile Robot Geometric Map with Photometric Information

  • Piotr Skrzypczyński
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 57)

Summary

In this paper we discuss methods to increase the discriminative properties of the laser-based geometric features used in SLAM by employing monocular vision data. Vertical edges extracted from images enable to estimate the length of partially observed line segments. Salient visual features are represented as the SIFT descriptors. These photometric features augment the 2D line segments extracted from the laser data and form a new feature type.

Keywords

Scale Invariant Feature Transform Vertical Edge Laser Data Scale Invariant Feature Transform Feature Scale Invariant Feature Transform Descriptor 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arras, K.O., Tomatis, N., Jensen, B.T., Siegwart, R.: Multisensor On-the-Fly Localization: Precision and Reliability for Applications. Robotics and Autonomous Systems 34, 131–143 (2001)CrossRefzbMATHGoogle Scholar
  2. 2.
    Castellanos, J.A., Tardós, J.D.: Mobile Robot Localization and Map Building. A Multisensor Fusion Approach. Kluwer, Boston (1999)CrossRefGoogle Scholar
  3. 3.
    Davison, A., Reid, I., Molton, N., Stasse, O.: MonoSLAM: Real-Time Single Camera SLAM. IEEE Trans. on Pattern Anal. and Machine Intell. 29(6), 1052–1067 (2007)CrossRefGoogle Scholar
  4. 4.
    Haralick, R.M.: Propagating Covariance in Computer Vision. Int. Journal Pattern Recog. and Artif. Intell. 10, 561–572 (1996)CrossRefGoogle Scholar
  5. 5.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Int. Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  6. 6.
    Newman, P., Ho, K.: SLAM-Loop Closing with Visually Salient Features. In: Proc. IEEE Int. Conf. on Robot. and Autom., Barcelona, pp. 644–651 (2005)Google Scholar
  7. 7.
    Ortin, D., Neira, J., Montiel, J.M.M.: Relocation Using Laser and Vision. In: Proc. IEEE Int. Conf. on Robot. and Autom., New Orleans, pp. 1505–1510 (2004)Google Scholar
  8. 8.
    Skrzypczyński, P.: Merging Probabilistic and Fuzzy Frameworks for Uncertain Spatial Knowledge Modelling. In: Kurzyński, M., et al. (eds.) Computer Recognition Systems, pp. 435–442. Springer, Berlin (2005)CrossRefGoogle Scholar
  9. 9.
    Skrzypczyński, P.: Spatial Uncertainty Management for Simultaneous Localization and Mapping. In: Proc. IEEE Int. Conf. on Robot. and Autom., Rome, pp. 4050–4055 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Piotr Skrzypczyński
    • 1
  1. 1.Institute of Control and Information EngineeringPoznań University of TechnologyPoznańPoland

Personalised recommendations