A SLAM Model Based on Omni-Directional Vision and Odometer

Abstract:

A new SLAM measurement model based on omni-directional vision and odometer is proposed in this paper. A virtual stereo vision composed of an omni-directional vision sensor and an odometer. Scale Invariant Feature Transform is used to extract stable and available vision features from the omni-images. The 3-D locations of these features are initialized by the pixel coordinates and the odometer data by stereo projection, and the locations will be corrected during the SLAM process when they are observed again. It is demonstrated that the new model can make a good accuracy with FastSLAM algorithm, and the accuracy is greatly improved corresponding to the classical vision sensor.

• Periodical: Applied Mechanics and Materials (Volume 103)
• Pages: 128-133
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.103.128
• Online since: September 2011
• Authors: Yu Quan Wang, Ying Hong Li
• Keywords: Measurement Model, Mobile Robot, Omni-Directional Vision, SIFT, SLAM

References

[1] Montemerlo M, Thrun S and Koller D, FastSLAM: A factored solution to the simultaneous localization and mapping problem, Proceedings of the Eighteenth National Conference on Artificial Intelligence, AAAI Press, Jul. 2002, pp.593-598.

[2] Fleck Sven, Busch Florian, Biber Peter and Straer Wolfgang, Omnidirectional 3D modeling on a mobile robot using graph cuts, Proceedings of IEEE International Conference on Robotics and Automation, IEEE Press, Apr. 2005, pp.1748-1754.

DOI: 10.1109/robot.2005.1570366

[3] Y. Boykov and V. Kolmogorov, An experimental comparison of mincut/max-flow algorithms for energy minimization in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE CS Press, Sep. 2004, p.1124.

DOI: 10.1109/tpami.2004.60

[4] A. J. Davison, D. W. Murray. Mobile robot localisation using active vision. Proceedings of the 5th European Conference on Computer Vision, Freiburg, 1998, 809-825P.

DOI: 10.1007/bfb0054781

[5] David G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol. 60, Nov. 2004, p.91–110, doi: 10. 1023/B: VISI. 0000029664. 99615. 94.

DOI: 10.1023/b:visi.0000029664.99615.94

[6] Lindeberg T, Scale-Space Theory in Computer Vision, Dordrecht, Netherlands: Kluwer Academic Publishers, (1994).

[7] Brown M and Lowe D, Invariant Features from Interest Point Groups, Proc. of the 13th British Machine Vision Conference (BMVC02), Cardiff, Wales, Sep. 2002, pp.656-665.

DOI: 10.5244/c.16.23

[8] Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell, Rao-blackwellised Particle Filtering for dynamic Bayesian networks, Proceedings of the Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00), 2000, pp.176-183.

[9] Murphy K P, Bayesian map learning in dynamic environments, Advances in Neural Information Processing System, MIT Press, vol. 12, 2000, pp.1015-1021.

[10] F. Linåker and M. Ishikawa, Real-Time Appearance-Based Monte Carlo Localization, Robotics and Autonomous Systems, vol. 54, Mar. 2006, p.205–220, doi: 10. 1016/j. robot. 2005. 11. 003.

DOI: 10.1016/j.robot.2005.11.003