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JRM Vol.22 No.2 pp. 140-149
doi: 10.20965/jrm.2010.p0140
(2010)

Paper:

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Robust Landmark Estimation and Unscented Particle Sampling for SLAM in Dynamic Outdoor Environment

Atsushi Sakai, Teppei Saitoh, and Yoji Kuroda

Department of Mechanical Engineering, Meiji University, 1-1-1 Higashimita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan

Received:
September 29, 2009
Accepted:
December 24, 2009
Published:
April 20, 2010
Creative Commons License
Keywords:
simultaneous localization and mapping, FastSLAM, landmark estimation, data association, unscented transformation
Abstract
In this paper, we propose a set of techniques for accurate and practical Simultaneous Localization And Mapping (SLAM) in dynamic outdoor environments. The techniques are categorized into Landmark estimation and Unscented particle sampling. Landmark estimation features stable feature detection and data management for estimating landmarks accurately, robustly, and at a low-calculation cost. The stable feature detection removes dynamic objects and sensor noise with scan subtraction, detects feature points sparsely and evenly, and sets data association parameters with landmark density. The data management calculates landmark existence probability and spurious landmarks are removed, utilizes landmark exclusivity for data association, and predicts importance weights using the observation range. Unscented particle sampling is based on Unscented Transformation for accurate SLAM. Simulation results of SLAM using our landmark estimation and experimental results of our SLAM in dynamic outdoor environments are presented and discussed. The results show that our landmark estimation decrease SLAM calculation time and maximum position error by 80% compared to conventional landmark estimation, and position estimation of SLAM with Unscented particle sampling ismore accurate than FastSLAM2.0 in dynamic outdoor environments.
Cite this article as:
A. Sakai, T. Saitoh, and Y. Kuroda, “Robust Landmark Estimation and Unscented Particle Sampling for SLAM in Dynamic Outdoor Environment,” J. Robot. Mechatron., Vol.22 No.2, pp. 140-149, 2010.
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References
  1. [1] S. Thrun, W. Burgard, and D. Fox, “Probabilistic Robotics,” MIT Press, 2005.
  2. [2] H. Durrant-Whyte and T. Bailey, “Simultaneous localization and mapping: part I,” IEEE Robotics & Automation Magazine, Vol.13, No.2, pp. 99-110, 2006.
  3. [3] T. Bailey and H. Durrant-Whyte, “Simultaneous localization and mapping (SLAM): part II,” Robotics & Automation Magazine, IEEE, Vol.13, No.3, pp. 108-117, 2006.
  4. [4] U. Freseand and G. Hirzinger, “Simultaneous localization and mapping-a discussion,” In Proc. of the Int. Conf. on Artificial Intelligence (IJCAI), 2001.
  5. [5] G. Dissanayake, P. Newman, S. Clark, H. F. Durrant-Whyte, and M. Csorba, “A solution to the simultaneous localisation and map building (SLAM) problem,” IEEE Trans. of Robotics and Automation, 2001.
  6. [6] D. M. Cole and P. M. Newman, “Using Laser Range Data for 3D SLAM in Outdoor Environments,” In Proc. of the IEEE Inter. Conf. on Robotics and Automation (ICRA ’06), Orlando, Florida, U.S.A., May 2006.
  7. [7] M. Kaess, A. Ranganathan, and F. Dellaert, “iSAM: Fast incremental smoothing and mapping with efficient data association,” in Proc. IEEE Int. Conf. Robot. Autom. (ICRA 2007), Roma, Italy, Apr. 10-14, pp. 1670-1677, 2007.
  8. [8] S. Thrun, D. Koller, Z. Ghahramani, H. Durrant-Whyte, and A. Y. Ng., “Simultaneous mapping and localization with sparse extended information filters,” In Proc. of WAFR, 2002.
  9. [9] J. Folkesson and H. Christensen, “Graphical SLAM for outdoor applications,” J. Field Robot., Vol.23, No.1, pp. 51-70, 705, 2006.
  10. [10] S. Thrun and M. Montemerlo, “The graphslam algorithm with applications to large-scale mapping of urban structures,” IJRR, Vol.25.
  11. [11] C. Kim, R. Sakthivel, and W. K. Chung, “Unscented FastSLAM: A robust algorithm for the simultaneous localization and mapping problem,” in Proc. IEEE Int. Conf. Robot. Autom., pp. 2439-2445, 2007.
  12. [12] K. Konolige and M. Agrawal, “Frameslam: From bundle adjustment to real-time visual mapping,” IEEE Trans. on Robotics, Vol.24, No.5, pp. 1066-1077, 2008.
  13. [13] T. D. Barfoot, “Online visual motion estimation using FastSLAM with SIFT features,” Edmonton, Alberta, August, pp. 3076-3082, 2005.
  14. [14] A. Diosi, G. Taylor, and L. Kleeman, “Interactive SLAM using laser and advanced sonar,” In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA ’05), Barcelona, Spain, April 2005.
  15. [15] M. Montemerlo, S. Thrun, “Simultaneous localization and mapping with unknown data association using FastSLAM,” Proc. ICRA, 2003.
  16. [16] J. Niento, J. Guivant, E. Nebot, and S. Thrun, “Real time data association in FastSLAM,” In Proc. of the IEEE Int. Conf. on Robotics and Automation, 2003.
  17. [17] M. Montemerlo, S. Thrun D. Koller, and B. Wegbreit, “FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges,” In Proc. of the Int. Conf. on Articial Intelligence (IJCAI), 2003.
  18. [18] M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, “Fast-SLAM: A factored solution to the simultaneous localization andmapping problem,” Proc. AAAI, 2002.
  19. [19] S. J. Julier, J. K. Uhlmann, and H. F. Durrant-Whyte, “A new approach for filtering nonlinear systems,” in Proc. Amer. Contr. Conf., Seattle, WA, June, pp. 1628-1632, 1995.
  20. [20] R. Merwe and E. Wan, “Sigma-point Kalman filters for probabilistic inference in dynamic state-space models,” In Proc. Workshop on Advances in Machine Learning, Montreal, Canada, June 2003.
  21. [21] R. Merwe, A. Doucet, N. Freitas, and E. Wan, “The Unscented Particle Filter,” Technical Report CUED/F-INFENG/TR 380, Cambridge University Engineering Department, 2000.
  22. [22] S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process., Vol.50, No.2, pp. 174-189, 2002.
  23. [23] G. Welch and G. Bishop, “An Introduction to the Kalman Filter,” TR-95-041, Dept. of Computer Science, Univ. of North Carolina, 2001.
  24. [24] D. Avitzour, “A maximum likelihood approach to data association,” IEEE Trans. on Aerospace and Electronics Systems, Vol.28, No.2, Apr., pp. 560-565, 1992.
  25. [25] MATLAB SLAM simulators by Tim Bailey
    URL http://www.acfr.usyd.edu.au/homepages/academic/tbailey/software/index.html
  26. [26] New Technology Foundation, “Tsukuba Challenge: Real World Robot Challenge [WWW page],”
    http://www.robomedia.org/challenge/ (in Japanese)
  27. [27] Hokuyo Automatic Co., Ltd.
    URL: http://www.hokuyo-aut.jp/

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